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Article A Scientometric Study of Neurocomputing Publications (1992–2018): An Aerial Overview of Intrinsic Structure

Manvendra Janmaijaya 1, Amit K. Shukla 1, Ajith Abraham 2 and Pranab K. Muhuri 1,* ID

1 Department of Computer Science, South Asian University, New Delhi 110021, India; [email protected] (M.J.); [email protected] (A.K.S.) 2 Machine Intelligence Labs (MIR Labs), 3rd Street NW, P.O. Box 2259, Auburn, WA 98071, USA; [email protected] * Correspondence: [email protected]

 Received: 11 May 2018; Accepted: 16 July 2018; Published: 19 July 2018 

Abstract: The international journal of neurocomputing (NC) is considered to be one of the most sought out journals in the computer science research fraternity. In this paper, an extensive bibliometric overview of this journal is performed. The bibliometric data is extracted from the (WoS) repository. The main objective of this study is to reveal internal structures and hidden inferences, such as highly productive and influential authors, most contributing countries, top institutions, collaborating authors, and so on. The CiteSpace and VOS viewer is used to visualize the graphical mapping of the bibliometric data. Further, the document co- network, cluster detection and references with strong burst is analyzed to reveal the intellectual base of NC publications.

Keywords: neurocomputing; bibliometric study; scientometric mapping; co-citation analysis; Web of Science

1. Introduction (also referred as ) as a research area has received tremendous attention from the scientific fraternity in recent times [1]. Bibliometrics is identified as an efficient technique to build a general outline of a certain scientific research field. There has been a rapid development in the field of bibliometrics in recent times attributing to social developments significantly gaining from the advancement of the internet and computers [2]. Scientometrics therefore gives an aerial view of scientific activity. It basically is a part of library and information science, but it has been judiciously used in numerous other research areas like management [3,4], energy & fuels [5,6], psychology [7,8], Dielectric and bioimpedance research [9], Selfish memes [10], and so on. Bibliometric study is not only celebrated in the field of computer science but is also finding increasing attention in domains such as decision support systems [11,12], bioinformatics [13,14], heuristics [15], big data [16–18], and software engineering [19]. Moreover, there has been a quality scientometric research work contribution in last few years, such as Fuzzy decision making [20], fuzzy theory research in China [21], Real-time operating systems [22], Linguistic decision making studies [23], Ordered weighted averaging operators [24], and Atanassov intuitionistic fuzzy sets [25]. Apart from subject specific bibliometric study, there are several journal specific studies, such as Knowledge Based Systems (KBS) [26], International Journal of Intelligent Systems (IJIS) [27], IEEE Transactions on Fuzzy Systems (TFS) [28], European Journal of Operations Research (EJOR) [29], Computers & Industrial Engineering [30], Information Sciences (INS) 1968–2016 [31,32], Medicine [33], Applied Soft Computing [34], and others [35–38].

Publications 2018, 6, 32; doi:10.3390/publications6030032 www.mdpi.com/journal/publications Publications 2018, 6, 32 2 of 22

Neurocomputing (NC) is one of the most pioneering journals in the field of neurocomputing. Throughout the years it has maintained a high reputation and is widely read by the computer science research fraternity. NC publishes various document types, such as articles, theoretical contributions, literature reviews, conference and workshop meeting reports, short communications, and so on. All these document types cover a wide area of computer science discipline including signal processing, speech processing, image processing, computer vision, control, robotics, optimization, scheduling, resource allocation, and financial forecasting. NC not only covers the theoretical aspects but also focuses on the practical contributions, such as simulation software environments, emulation hardware architectures, models of concurrent computation, neurocomputers, and neurochips (digital, analog, optical, and biodevices) [39]. The popularity of NC journal can be observed from the this journal has acquired in such short span of time. Impact factor (IF) is the measure of the frequency with which an article of the journal is cited in year. It is a journal metric used for analyzing the rank of a journal. According to the latest Journal Citation Reports of the Web of Science, which is owned by Clarivate Analytics, NC journal has an impact factor of 3.317. The major contribution of this paper are as follows:

1. This paper reports a detailed study of the generic bibliometric impression of the journal of NC publications by examining the most prominent articles, institutions, authors, author collaborations, institute collaborations, country collaborations, etc. 2. The purpose of this study is to provide an intrinsic structure of NC publications and the targeted research areas where the contributions are made. 3. A detailed study of the NC publication base is also performed to understand the knowledge base of NC publications. 4. The top cited articles in this journal are listed to give highlights of the NC publications. 5. The data of all the documents considered for this study have been retrieved from the Web of Science (WoS) database which comprises of several document types, such as articles, proceeding papers, letters, editorials, and reviews. 6. Statistical techniques and analysis determines the nature of the NC citations and their structure.

The paper is organized as follows: Section2 explains the data collection methodology, performance indicators, and the visualization tools used in this paper. In Section3, we have covered the publications and citation structure, most productive and influential authors, top ranked countries in NC, and the best institutions contribution in NC. The visualization of the publication structure of the various entities (authors, countries, institutions, etc.) is shown with the help of the Citespace. Then we have explained the bibliometric landscape and included the document co-citation analysis in Sections4 and5 , simultaneously. Finally, a conclusion is drawn in Section6 followed by the references used in this paper.

2. Methodology and Performance Indicators The repository of Web of Science (WoS) is utilized for this study, which is a scientific citation indexing service maintained by Clarivate Analytics. WoS is the first multidisciplinary bibliographic index of journal publications designed. It is considered a standard data source for bibliometrics. Other databases such as by Elsevier are also used by bibliometricians. The first volume of NC came in the year 1989, however, the data for this study has been extracted from WoS where NC’s publications are from the year 1992. Therefore the data span of this study is from 1992 to 2018. The data was collected in December 2017. A total of 10,838 publications were retrieved for the above said period. The query used in the search engine of WoS was “SO = Neurocomputing”. Each record of the data retrieved from WoS comprises a number of fields such as author, author affiliation, title, , citations record, and so on. Publications 2018, 6, 32 3 of 22

This study uses a range of scientometric indicators combined with statistical analysis and visualizations tools. Associations between several entitles/variables (author, organization, or country) have been explored, which are defined as follows:

(i) Co-citation analysis: This is another way to analyze the citation structure and provides a glimpse of the relationships between papers, and through them other entities, inside a research domain. It basically tells us that if two entities are co-cited, i.e., cited together more frequently then there are closer academic or disciplinary ties between them. (ii) Bibliographic coupling: This is the opposite of co-citation, it is the number of times two or more entities cite the same entity. Both co-citation and bibliographic coupling indicate disciplinary links. The number of co-authored documents identifies collaborative or co-authorship links between two entities, directly linking authors, institutions, or countries. (By entity we mean either an author, an organization, or a country.) (iii) Document co-citation analysis (DCA): This explores the citation base of publications giving an insight into the inspiration corpus.

Performance Indicators and Visual Tools: Now, we explain the various scientometric indicators used for the analysis.

(i) Total Papers (TP)—the total number of papers from a particular source, (ii) Total Citations (TC)—the total number of citations generated by a particular publication within the database of WoS. (iii) Citations per Paper (CPP)—TP divided by TC, (iv) Hirsch index or h-index—This is equal to the number of papers (N) of an entity that has more than N citations each [40].

Further, two of the widely used graphical mapping software’s are used, namely, VOSviewer and CiteSpace

(i) VOSviewer is a tool for generation and visualization of bibliographic networks. It has been used here for depicting bibliographic coupling and co-authorship between different entities VOSviewer can be used to construct networks of scientific publications, scientific journals, researchers, research organizations, countries, keywords, or terms. Items in VOSviewer are visualized in terms of nodes and edges connecting theses nodes. Items in these networks can be connected by co-authorship, co-occurrence, citation, bibliographic coupling, or co-citation links. Examples are bibliographic coupling links between publications, co-authorship links between researchers, and co-occurrence links between terms. Vosviewer version 1.6.5 has been used for this study. (ii) CiteSpace [41] has also been used here for visualization and analysis document co-citation of NC publications. It is an open source Java application used for visualizing trends from metadata of scientific literature. Citespace version 5.0 R4 SE has been used for this study.

3. Citation Structure of NC Publications In the year 1992, NC published 29 research articles. Since 1998, the number of publications never dropped below 100 and the trends of publications are mostly in the increasing order. The maximum number of 1840 articles were published in the year 2016. The years from 2015–2017 account for approximately 40% of the total NC publications. In the year 2017, there were 1118 publications up to the month of November, when the search query was performed, and this count will certainly increase. For 2018, 143 papers are already included in NC. There is a strong possibility that the journal is expected to follow the current increasing trend over the years to come. Figure1 shows the distribution of citations of NC publications over the years (1992–2018), which received a total of 106,536 citations. Interestingly, the year 1992 reflects only 30 citations with a total of 29 publications. The year 1998 accounted for a citation count of 3361 with only 108 publications, Publications 2018, 6, 32 4 of 22 making this the year with the maximum citations per year. However, the maximum number of citations were attracted in the year 2015, with a total count of 9108. The TC is not shown for 2018 as counting is yetPublications to start. 2018 Moreover,, 6, x FOR PEER the REVIEW year 2006, with 335 publications, attracted comparable citations4 of (7617) 21 to thatPublications which 2018 2016, 6, x publicationsFOR did. On further analysis, this is found to be because of the article4 of 21 by article by Huang et al. [31] where extreme machine learning is presented along with its theory and Huangapplications. et al. [31 ] where extreme machine learning is presented along with its theory and applications. article by Huang et al. [31] where extreme machine learning is presented along with its theory and applications. Total Publications Total Publications 1840 1840 1301 1118 1301 902 1118 702 902 427 702 414 393 369 345 342 335 308 295 257 255 252 219 184 427 143 414 393 108 103 369 74 345 342 62 335 39 29 308 22 295 257 255 252 219 184 143 108 103 74 62 39 29 22 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Years 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Figure 1. Neurocomputing (NC)Years publications/year distribution. Figure 1. Neurocomputing (NC) publications/year distribution. Figure 1. Neurocomputing (NC) publications/year distribution. The diversity in the total number of citations with respect to the total number of publications overThe the diversity years is inshown the total in Figure number 2. There of citations has been with a gradual respect toincrease the total in numberthe citations of publications over the year. over The diversity in the total number of citations with respect to the total number of publications theIt years is at a is maximum shown in Figurein 19982 .with There a value has been of 31.21 a gradual as seven increase publications in the citationshave attracted over the more year. than It is100 at a over the years is shown in Figure 2. There has been a gradual increase in the citations over the year. maximumcitations inup 1998 to the with date a that value data of was 31.21 collected as seven for publications this study. It have is followed attracted by the more publications than 100 citations in the It is at a maximum in 1998 with a value of 31.21 as seven publications have attracted more than 100 years 2006 and 2003 with CPP of 22.7 and 21.5, respectively. This can also be attributed to the fact that upcitations to the date up to that the datadate wasthat data collected was collected for this study.for this Itstudy. is followed It is followed by the by publications the publications in the in yearsthe a lot of publications in these years have attracted a good number of citations. However, the citations 2006years and 2006 2003 and with 2003 CPP with of CPP 22.7 of and 22.7 21.5,and 21.5, respectively. respectively. This This can can also also be be attributed attributed to to thethe fact that that a per year count has degraded in the last few years as shown in Figure 3. This can possibly be because lota of lot publications of publications in thesein these years years have have attracted attracted a a good good numbernumber of citation citations.s. However, However, the the citations citations publications in the later years are fairly new and possibly will attract more citations in the times to perper year year count count has has degraded degraded in in the the last last fewfew yearsyears asas shown in in Figure Figure 3.3. This This can can possibly possibly be be because because come. publicationspublications in in the the later later yearsyears areare fairly new new and and possibly possibly will will attract attract more more citations citations in the in times the times to to come. Total Citations 9108 8460

Total Citations 8168 9108 7617 7253 8460 7085 8168 7617 5831 7253 5815 5717 5592 7085 5399 5429 5831 5815 5717 5592 4327 5399 3802 5429 3361 3075 4327 3802 2233 2186 3361 1736 3075 1407 2233 982 2186 516 765 462 1736 180 30 1407 982 516 765 462 180 30 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Years

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Figure 2. Citations structureYears of NC over the years. Figure 2. Citations structure of NC over the years. Table 1 displays the generalFigure 2.citationCitations structure structure of NC of NC publications over the years. from 1992 to 2018. Here, ‘%’ is used to represent the percentage. There are 16 papers which received more than 200 citations, which Table 1 displays the general citation structure of NC publications from 1992 to 2018. Here, ‘%’ is is 0.14% of the total NC publications. With more than 100 citations, we have 56 publications which used to represent the percentage. There are 16 papers which received more than 200 citations, which account for 0.51% of total publications. There are 2146 papers with no citations at all, which is 19.8% is 0.14% of the total NC publications. With more than 100 citations, we have 56 publications which of total publications. Approximately 80% of the publications had at least 1 citation; among them, a account for 0.51% of total publications. There are 2146 papers with no citations at all, which is 19.8% of total publications. Approximately 80% of the publications had at least 1 citation; among them, a

Publications 2018, 6, x FOR PEER REVIEW 5 of 21

major chunk was published in the last five years and thus this number is bound to change as they are Publicationsfairly young2018, 6 ,publications. 32 The year 1998 stands at the top position with the CPP. Almost 50% of NC5 of 22 publications have attracted more than five citations which indicates the reach of the journal.

35 Citations Per Year 30 25 20 15 10 5 0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Years

Figure 3. Citations per year of the NC computing. Figure 3. Citations per year of the NC computing. Table 1. General citation Structure in the Journal: NC (1992–2018).

TableYear 1 displays≥200 the≥100 general≥50 citation≥20 structure≥10 ≥ of5 NC publications≥1 0 fromTP 1992h-Index to 2018.TC Here, CPP ‘%’ is used to2018 represent 0 the percentage. 0 0 There0 are 160 papers0 which0 received143 more143 than 200 0 citations, 0 which0.00 is 0.14%2017 of the total0 NC 0 publications. 0 1 With more4 than9 100264 citations, 854 we1118 have 56 7 publications 510 0.46 which 2016 0 0 3 21 93 419 1389 451 1840 20 5813 3.16 account2015for 0.51%0 of total 0 publications. 1 73 There 322 are 7152146 papers1200 with101 no1301 citations 27 at all, which 9190 is7.06 19.8% of total2014 publications. 0 1 Approximately 8 106 80%321 of the576 publications 845 had57 at 902 least 1 citation; 31 8495 among 9.42 them, 2013 0 2 13 115 296 468 663 39 702 35 8200 11.68 a major2012 chunk 0 was published 0 14 in the 89 last five207 years296 and thus398 this29 number 427 is bound 36 to change 5826 13.64 as they 2011 1 1 20 91 189 260 350 19 369 33 5605 15.19 are fairly2010 young1 publications. 2 19 The year 94 1998187 stands261 at the329top position16 345 with the 36CPP. Almost 5840 16.93 50% of NC publications2009 0 have 2 attracted 27 more 124 than five235 citations313 which389 indicates 25 414 the reach 41 of the 7095 journal. 17.14 2008 1 7 30 115 199 277 367 26 393 40 7261 18.48 Neurocomputing2007 1 4 journal 24 has been 75 publishing151 227 quality322 and20 impactful 342 research 35 articles 5405 since15.80 its induction.2006 Table1 2 displays 3 a 16 list of top 82 40153 most cited219 articles305 as30 indexed 335 by WoS. 35 All 7627 the papers22.77 in 2005 1 4 17 53 97 154 233 22 255 32 3806 14.93 this list2004 got more0 than 0 100 citations. 8 Huang 46 104 et al. [16142 ] received261 the34 highest295 citations 27 of 2762 3077 since10.43 2006 for the2003 paper titled4 “Extreme 9 learning21 57 machine: 92 Theory131 and210 applications” 42 .252 It has received 33 5439 citations 21.58 with 2002 2 5 11 47 102 162 257 51 308 31 4331 14.06 increasing2001 speed1 and has 1 a count 6 of 230.17 30 citations 67 108 per year,230 which27 is highest257 in 22 the top 2234 40 list8.69 of most cited publications.2000 0 This 2 research 10 article 27 proposed 56 a97 novel185 learning 34 algorithm219 for 24 single 2193 feed forward10.01 1999 0 1 5 25 47 82 162 22 184 24 1737 9.44 neural1998 networks 2 where 7 the hidden 17 40 nodes 58 are randomly79 96 chosen12 and108 the weights 30 are 3365 analytically 31.16 assigned.1997 The0 second 1 position 3 is acquired 17 26 by paper39 “Time59 series15 forecasting74 using 18 a hybrid 982 13.27 ARIMA 1996 1 1 8 21 34 47 69 34 103 20 1407 13.66 and neural1995 network0 model” 1 authored 3 by 11 Zhang22 [43]31 where 52 he proposed10 a62 hybrid 15 approach 765 containing12.34 autoregressive1994 0 integrated 2 moving 3 average 7 11 and artificial15 25 neural 14 networks 39 forlinear 10 and 516 non-linear13.23 1993 0 0 1 3 3 9 15 7 22 6 180 8.18 modelling1992 for forecasting.0 0 These 0 two1 are followed3 by9 Suykens17 et12 al. [4429], Kim [45 6 ], and 106 Kohonen 3.66 [46], for theirTotal papers16 titles 56 as follows: 288 Weighted 1371 3079 least squares5164 support8692 2146 vector 10,838 machines: robustness and sparse approximation% ;0.14Financial 0.51 time series 2.65 forecasting 12.64 28.40 using 47.64 support 80.19 vector 19.8 machines ; and The self-organizing map, respectively. The paper by Suykens et al. [44] proposed a method for obtaining robust estimates for Neurocomputing journal has been quality and impactful research articles since its regressioninduction. by Table applying 2 displays a weighted a list of version top 40 ofmost least cited squares articles support as indexed vector by machine WoS. All (LS-SVM) the papers along in withthis a sparselist got approximationmore than 100 citations. procedure Huang for weighted et al. [42] and received unweighted the highest LS-SVM. citations Kim of [ 452762] examined since 2006 the feasibilityfor the paper of support titled vector“Extreme machines learning inmachine: financial Theory forecasting. and applications” Kohonen. It [has46] gavereceived an overviewcitations with of the self-organizingincreasing speed map and algorithm, has a count namely, of 230.17 a software citations for per visualizing year, which high is dimensionhighest in data.the top 40 list of mostThere cited are publications. six papers which This wereresearch published article proposed before 2000, a novel with thelearning rest of algorithm them published for single after feed 2000. Fromforward the last neural five years,networks there where are two the papers hidden which node ares are in randomly the list of topchosen 40 most and citedthe weights list. They are are “A surveyanalytically on fall assigned. detection: The Principles second andposition approaches” is acquiredand “Weightedby paper “Time extreme series learning forecasting machine using for a imbalance hybrid learning” from Mubashir et al. [47] and Zong et al. [48], respectively. Publications 2018, 6, 32 6 of 22

Table 1. General citation Structure in the Journal: NC (1992–2018).

Year ≥200 ≥100 ≥50 ≥20 ≥10 ≥5 ≥1 0 TP h-Index TC CPP 2018 0 0 0 0 0 0 0 143 143 0 0 0.00 2017 0 0 0 1 4 9 264 854 1118 7 510 0.46 2016 0 0 3 21 93 419 1389 451 1840 20 5813 3.16 2015 0 0 1 73 322 715 1200 101 1301 27 9190 7.06 2014 0 1 8 106 321 576 845 57 902 31 8495 9.42 2013 0 2 13 115 296 468 663 39 702 35 8200 11.68 2012 0 0 14 89 207 296 398 29 427 36 5826 13.64 2011 1 1 20 91 189 260 350 19 369 33 5605 15.19 2010 1 2 19 94 187 261 329 16 345 36 5840 16.93 2009 0 2 27 124 235 313 389 25 414 41 7095 17.14 2008 1 7 30 115 199 277 367 26 393 40 7261 18.48 2007 1 4 24 75 151 227 322 20 342 35 5405 15.80 2006 1 3 16 82 153 219 305 30 335 35 7627 22.77 2005 1 4 17 53 97 154 233 22 255 32 3806 14.93 2004 0 0 8 46 104 161 261 34 295 27 3077 10.43 2003 4 9 21 57 92 131 210 42 252 33 5439 21.58 2002 2 5 11 47 102 162 257 51 308 31 4331 14.06 2001 1 1 6 30 67 108 230 27 257 22 2234 8.69 2000 0 2 10 27 56 97 185 34 219 24 2193 10.01 1999 0 1 5 25 47 82 162 22 184 24 1737 9.44 1998 2 7 17 40 58 79 96 12 108 30 3365 31.16 1997 0 1 3 17 26 39 59 15 74 18 982 13.27 1996 1 1 8 21 34 47 69 34 103 20 1407 13.66 1995 0 1 3 11 22 31 52 10 62 15 765 12.34 1994 0 2 3 7 11 15 25 14 39 10 516 13.23 1993 0 0 1 3 3 9 15 7 22 6 180 8.18 1992 0 0 0 1 3 9 17 12 29 6 106 3.66 Total 16 56 288 1371 3079 5164 8692 2146 10,838 % 0.14 0.51 2.65 12.64 28.40 47.64 80.19 19.8

Table 2. Top 40 cited publications in NC.

Rank Title Authors Year TC Citations per Year 1 Extreme learning machine: Theory and applications Huang GB; Zhu QY; Siew CK 2006 2762 230.17 Time series forecasting using a hybrid ARIMA and 2 Zhang GP 2003 660 44.00 neural network model Weighted least squares support vector machines: Suykens JAK; De Brabanter J; 3 2002 615 38.44 robustness and sparse approximation Lukas L; Vandewalle J Financial time series forecasting using support 4 Kim KJ 2003 463 30.87 vector machines 5 The self-organizing map Kohonen T 1998 462 23.10 6 Convex incremental extreme learning machine Huang GB; Chen L 2007 454 41.27 Enhanced random search based incremental extreme 7 Huang GB; Chen L 2008 376 37.60 learning machine Blind separation of convolved mixtures in the 8 Smaragdis P 1998 361 18.05 frequency domain Optimization method based extreme learning 9 Huang GB; Ding XJ; Zhou HM 2010 335 41.88 machine for classification Designing a neural network for forecasting financial 10 Kaastra I; Boyd M 1996 322 14.64 and economic time series 11 The support vector machine under test Meyer D; Leisch F; Hornik K 2003 291 19.40 Evaluation of simple performance measures for 12 Duan K; Keerthi SS; Poo AN 2003 280 18.67 tuning SVM hyperparameters (2D)(2)PCA: Two-directional two-dimensional PCA 13 Zhang DQ; Zhou ZH 2005 265 20.38 for efficient face representation and recognition Publications 2018, 6, 32 7 of 22

Table 2. Cont.

Rank Title Authors Year TC Citations per Year An approach to blind source separation based on 14 Murata N; Ikeda S; Ziehe A 2001 240 14.12 temporal structure of speech signals Error-backpropagation in temporally encoded Bohte SM; Kok JN; 15 2002 235 14.69 networks of spiking neurons La Poutre H Recent advances and trends in visual tracking: Yang HX; Shao L; Zheng F; 16 2011 229 32.71 A review Wang L; Song Z Modified support vector machines in financial time 17 Tay FEH; Cao LJ 2002 194 12.13 series forecasting Support vector machines experts for time 18 Cao LJ 2003 192 12.80 series forecasting A comparison of PCA, KPCA and ICA for Cao LJ; Chua KS; Chong WK; 19 2003 191 12.73 dimensionality reduction in support vector machine Lee HP; Gu QM Empirical evaluation of the improved Rprop 20 Igel C; Husken M 2003 188 12.53 learning algorithms Determination of the spread parameter in the Wang WJ; Xu ZB; Lu WZ; 21 2003 186 12.40 Gaussian kernel for classification and regression Zhang XY 22 Evolutionary tuning of multiple SVM parameters Friedrichs F; Igel C 2005 185 14.23 A survey on fall detection: Principles 23 Mubashir M; Shao L; Seed L 2013 174 34.80 and approaches WEBSOM - Self-organizing maps of Kaski S; Honkela T; Lagus K; 24 1998 169 8.45 document collections Kohonen T Asymptotic and robust stability of genetic regulatory 25 Ren F; Cao J 2008 168 16.80 networks with time-varying delays 26 Natural Actor-Critic Peters J; Schaal S 2008 167 16.70 Artificial neural networks in hardware A survey of 27 Misra J; Saha I 2010 165 20.63 two decades of progress Learning And Generalization Characteristics Of The 28 Pao YH; Park GH; Sobajic DJ 1994 163 6.79 Random Vector Functional-Link Net Schreiber S; Fellous JM; A new correlation-based measure of spike 29 Whitmer D; Tiesinga P; 2003 158 10.53 timing reliability Sejnowski TJ Time-series prediction using a local linear wavelet 30 Chen YH; Yang B; Dong JW 2006 156 13.00 neural network A fast pruned-extreme learning machine for Rong HJ; Ong YS; Tan AH; 31 2008 154 15.40 classification problem Zhu ZX The nonlinear PCA learning rule in independent 32 Oja E 1997 154 7.33 component analysis Weighted extreme learning machine for Zong WW; Huang GB; 33 2013 147 29.40 imbalance learning Chen YQ A case study on using neural networks to perform 34 Yao JT; Tan CL 2000 143 7.94 technical forecasting of forex Hopfield neural networks for optimization: study of Joya G; Atencia MA; 35 2002 142 8.88 the different dynamics Sandoval F Li MB; Huang GB; 36 Fully complex extreme learning machine Saratchandran P; 2005 140 10.77 Sundararajan N Ensemble of online sequential extreme 37 Lan Y; Soh YC; Huang GB 2009 139 15.44 learning machine Multimodality medical image fusion based on 38 multiscale geometric analysis of Yang L; Guo BL; Ni W 2008 133 13.30 contourlet transform A comprehensive review of current local features for 39 Li J; Allinson NM 2008 127 12.70 computer vision Sorjamaa A; Hao J; Reyhani N; 40 Methodology for long-term prediction of time series 2007 125 11.36 Ji YN; Lendasse A Publications 2018, 6, 32 8 of 22

Top Authorship, Country, and Institutions The list of most productive and influential authors is shown in Table3. The most productive authors are ordered by the maximum number of TP while the most influential authors are accounted by TC. The table also describes authors with total citations, citations per paper and h-index. Cao and Yang both have 74 publications, however, Cao received more citations of 1574 which is higher than Yang’s citation of 510. These two are followed by Wang (TP = 72) and Zhang (TP = 64). Li and Liu published same number of papers with a count of 60.

Table 3. Most Productive and Influential Authors in NC (1992–2018).

Rank Name Country Affiliation TP TC CPP h-Index 1 Cao JD China Southeast University 74 1574 21.27 23 2 Yang J China Beijing Institute of Technology 74 510 6.89 11 3 Wang J China Tsinghua University 72 374 5.19 12 4 Zhang HG China Northeastern University 64 899 14.05 17 5 Li J China Xidian University 60 528 8.80 13 6 Liu Y China Northeastern University 60 495 8.25 14 7 Zhang J China Beijing Institute of Technology 58 429 7.40 10 8 Wang Y China Central South University 55 344 6.25 10 9 Zhang Y China Tongji University 54 448 8.30 11 10 Yuan Y China Chinese Academy of Sciences 52 615 11.83 15 11 Li XL China Chinese Academy of Sciences 50 461 9.22 12 12 Sanchez VD USA University of Miami 50 197 3.94 7 13 Wang D China Dalian University of Technology 49 565 11.53 13 14 Wang L China Wuhan University 48 662 13.79 12 15 Liu J China Qingdao Agricultural University 45 215 4.78 8 16 Huang TW Qutar Texas A&M University 44 496 11.27 13 17 Liu YR China Yangzhou University 43 712 16.56 14 18 Zhang H China PLA University of Science & Technology 43 365 8.49 11 19 Zhang L China Hong Kong Polytechnic University 43 323 7.51 12 20 Gao XB China Xidian University 42 362 8.62 12

Again, Cao tops the list of most influential authors with TC of 1574. Zhang HG, Liu YR, Wang L and Yuan Y are next in the list with TC of 899, 712, 662, and 615, respectively. The maximum citations per paper (CPP) were attained by Cao (21.27), Liu (16.56), Zhang (14.05), Wang (13.79), and Yuan (11.83). The h-index is again highest for Cao (23). Zhang (17) and Yuan (15) comes second and third highest in the list. They are followed by Liu YR and Liu Y with same h-index of 14. Figure4 conveys similar information as in Table3, for example the circles representing Cao and Zhang are the biggest followed by Wang and Huang. Further, it depicts the author co-citation network and thus describes the disciplinary ties between them. The researchers from various countries are publishing in one of the computer sciences leading journal of neurocomputing. The list of leading 25 countries which are publishing in NC is shown in Table4. Apart from total papers, the table also mentions other indicators such as: total citations, citations per paper, and h-index. The countries are ordered according to the total number of publications. The People’s Republic of China is the most productive country which lies on the first spot with a total publication count of 5179. It is followed by the USA (1498), England (590), Spain (564), and Germany (492). Surprisingly, there is no developing country in the list of top 10 most productive countries. India Ranks 11th in the list, which is a developing nation with 277 publications. The ranking of most influential countries remains the same with respect to the most productive countries for China (TC = 45,688) and the USA (TC = 13,933). Interestingly, then comes Singapore with 8764 citations in only 278 publications. It is then followed by England (6596), Germany (5145), and Spain (4880). The citations per paper shows a different image of the influence of the countries. Singapore tops the Publications 2018, 6, x FOR PEER REVIEW 8 of 21

19 Zhang L China Hong Kong Polytechnic University 43 323 7.51 12 20 Gao XB China Xidian University 42 362 8.62 12

Again, Cao tops the list of most influential authors with TC of 1574. Zhang HG, Liu YR, Wang L and Yuan Y are next in the list with TC of 899, 712, 662, and 615, respectively. The maximum citations per paper (CPP) were attained by Cao (21.27), Liu (16.56), Zhang (14.05), Wang (13.79), and Yuan (11.83). Publications 2018, 6, 32 9 of 22 The h-index is again highest for Cao (23). Zhang (17) and Yuan (15) comes second and third highest in the list. They are followed by Liu YR and Liu Y with same h-index of 14. Figure 4 conveys listsimilar with information the highest as CPP in ofTable 31.53. 3, for Then example comes the Belgium circles (17.58), representing Finland Cao (16.34), and Zhang and The are Netherlands the biggest (12.7).followed These by Wang three countriesand Huang. were Further, not even it depicts in the listtheof author top 10 co-citation most productive network countries. and thus Theredescribes is a totalthe disciplinary of 12 countries ties whosebetween CPP them. is more than 10.

Figure 4. Author Co-Citation Analysis.

The researchers from various countries are publishing in one of the computer sciences leading Table 4. journal of neurocomputing. MostThe list Productive of leading Countries 25 countries publishing which in NC are (1992–2018). publishing in NC is shown in Table 4. Apart from total papers, the table also mentions other indicators such as: total citations, Rank Country TP TC CPP h-Index citations per paper, and h-index. The countries are ordered according to the total number of publications. 1 Peoples R China 5179 45,688 8.82 63.00 2 USA 1498 13,933 9.30 46.00 3 England 590 6596 11.18 37.00 4 Table 4. MostSpain Productive Countries 564 publishing 4880 in NC (1992–2018). 8.65 31.00 5 Germany 492 5145 10.46 30.00 Rank Country TP TC CPP h-Index 6 Japan 439 3584 8.16 27.00 7 1 FrancePeoples R China 346 5179 45,688 3909 8.82 11.30 63.00 30.00 8 2 AustraliaUSA 3431498 13,933 2846 9.30 8.3046.00 25.00 9 3 ItalyEngland 294590 6596 2825 11.18 9.6137.00 26.00 10 4 SingaporeSpain 278564 4880 8764 8.65 31.5331.00 35.00 11 5 IndiaGermany 277492 5145 3046 10.46 11.0030.00 26.00 12 6 South KoreaJapan 276439 3584 3016 8.16 10.9327.00 27.00 13 7 BrazilFrance 256346 3909 1899 11.30 7.4230.00 21.00 14 8 CanadaAustralia 253343 2846 2481 8.30 9.8125.00 27.00 15 Taiwan 244 2516 10.31 25.00 9 Italy 294 2825 9.61 26.00 16 Iran 174 1511 8.68 20.00 10 Singapore 278 8764 31.53 35.00 17 Saudi Arabia 170 1550 9.12 21.00 18 11 FinlandIndia 149277 3046 2434 11.00 16.3426.00 23.00 19 12 PolandSouth Korea 139 276 3016 1427 10.93 10.27 27.00 23.00 20 Belgium 118 2074 17.58 21.00 21 The Netherlands 111 1410 12.70 20.00 22 Scotland 104 840 8.08 16.00 23 Mexico 101 686 6.79 15.00 24 Greece 86 900 10.47 17.00 25 Malaysia 77 393 5.10 10.00 Publications 2018, 6, x FOR PEER REVIEW 9 of 21

13 Brazil 256 1899 7.42 21.00 14 Canada 253 2481 9.81 27.00 15 Taiwan 244 2516 10.31 25.00 16 Iran 174 1511 8.68 20.00 17 Saudi Arabia 170 1550 9.12 21.00 18 Finland 149 2434 16.34 23.00 19 Poland 139 1427 10.27 23.00 20 Belgium 118 2074 17.58 21.00 21 The Netherlands 111 1410 12.70 20.00 22 Scotland 104 840 8.08 16.00 23 Mexico 101 686 6.79 15.00 24 Greece 86 900 10.47 17.00 25 Malaysia 77 393 5.10 10.00

The People’s Republic of China is the most productive country which lies on the first spot with a total publication count of 5179. It is followed by the USA (1498), England (590), Spain (564), and Germany (492). Surprisingly, there is no developing country in the list of top 10 most productive countries. India Ranks 11th in the list, which is a developing nation with 277 publications. The ranking of most influential countries remains the same with respect to the most productive countries for China (TC = 45,688) and the USA (TC = 13,933). Interestingly, then comes Singapore with 8764 citations in only 278 publications. It is then followed by England (6596), Germany (5145), and Spain (4880). The citations per paper shows a different image of the influence of the countries. Singapore

Publicationstops the list2018 with, 6, 32 the highest CPP of 31.53. Then comes Belgium (17.58), Finland (16.34), and10 ofThe 22 Netherlands (12.7). These three countries were not even in the list of top 10 most productive countries. There is a total of 12 countries whose CPP is more than 10. With respect toto thethe h-index, ChinaChina topstops thethe list with a value of 63 followed by USA (46), England (37),(37), SingaporeSingapore (35), and Spain (31). Then France and and Germany Germany share share the the same same h-index h-index value value of of 30. 30. The dominance of China can also be seen in thethe FigureFigure5 5 which which describes describes thethe co-citationco-citation analysisanalysis ofof countries. The inference from this visualization shows that, almost every time, a paperpaper originatingoriginating from ChinaChina isis citedcited alongalong withwith paperspapers originatingoriginating fromfrom otherother countries.countries.

Figure 5. Countries Co-Citation Analysis.

The institution-wise ordering is displayed in Table 5. This table shows the total publications, The institution-wise ordering is displayed in Table5. This table shows the total publications, total citations, citations per paper and h-index of institutions. Table 5 presents the most productive total citations, citations per paper and h-index of institutions. Table5 presents the most productive institutions which have contributed to NC publications. The Chinese Academy of Sciences, China, institutions which have contributed to NC publications. The Chinese Academy of Sciences, China, produced 526 total papers with total citations of 4752. The second spot is taken by Harbin Institute of Technology, also from China, with 219 papers. Southeast University from China takes the third spot with 206 occurrences. The total papers from the institute with total citations and citations per paper (CPP) are also given in the table, along with the h-index of each institution. As can be seen, most of the institutes in the list belong to China. Interestingly, most of the universities in this table are from China. However in terms of TC, Nanyang technological university, China stands at the top with 6862 citations followed by Chinese Academy of Sciences (4752) and Southeast University (3267). The institutes with the highest average citation per paper (CPP) were Nanyang Technological University, Singapore (46.68), National University of Singapore (27.46), and Southeast University (15.86). There are 11 institutions in this list of top 25 which have a CPP of at least 10. Two institutes have an h-index of 31, Chinese Academy of Sciences and Southeast University, both are from China. They are followed by the Nanyang Technological University (30), Harbin Institute of Technology, China (23), and Northeastern University, China (23). The dominance of China is also evident here as most of the institutions in Table5 are from China. However, among the institutions, as shown by the institution co-citation network in Figure6, the dominance of the Chinese Academy of Sciences can be concluded, which overlaps with almost all the other institutions. This determines that almost every time the publications from Chinese Academy of Sciences are cited along with all other institutions. Publications 2018, 6, 32 11 of 22 Publications 2018, 6, x FOR PEER REVIEW 11 of 21

Figure 6. Institution Co-Citation Analysis. Figure 6. Institution Co-Citation Analysis.

4. Bibliographic Landscape Table 5. Most Productive and Influential Institutions in NC (1992–2018). This section demonstrates the disciplinary links with the help of bibliographic coupling. The VOSviewerRank visualization tool Institute is used (Countries) to show the coupling between TP the authors, TC countries CPP h-Index and the institutions.1 Chinese Academy of Sciences, China 526 4752 9.03 31 2We have first shownHarbin the Institute bibliographic of Technology, coupling China between the authors 219 2123in Figure 9.69 7. The authors 23 3 are connected by the linkSoutheast with different University widths. China, More China width shows more 206 intersection 3267 15.86 or references 31 of 4 Nanjing University of Science Technology, China 180 1943 10.79 22 these5 authors, orHuazhong degree of University overlap of between Science Technology,the reference China lists, and 169thus more 1662 coupling. 9.83 Moreover, 21 each6 node representing theTsinghua authors University, is also marked China with three colors, 166red, blue 1243 and green. 7.49 Each 19 color consisting7 of group ofShanghai countries Jiao forms Tong a University,cluster whic Chinah represents that 164these countries 1508 9.20in a cluster 21 have cited8 each other’s paper withXidian more University, frequency. China In the prominent cluster 158 shown 1178 in red 7.46 color, Wang 19 J, 9 Zhejiang University, China 158 1167 7.39 18 Yang10 J, Yuan Y are the authorsNortheastern whose University, papers are China more cited by the other 157 authors 1492 in 9.50that cluster. 23 The Other11 clusterUniversity is of a green Of Electronic color with Science authors Technology such as: of China,Zhang China J, Wang149 D, Zhang 1395 HG, Zhang 9.36 Y, and 21 so on. Hammer12 B, ChenNanyang I, Wang Technological GR, and so University, on form the Singapore third cluster of a 147 blue color. 6862 We 46.68have mentioned 30 the author’s13 name, withXi the An Jiaotonglast name University, followed China by the initials of the 136 first 1588name as 11.68 there are 17many 14 Beihang University, China 132 884 6.70 16 Chinese authors whose names match closely. 15 Centre National De La Recherche Scientifique CNRS, France 128 1704 13.31 22 16Figure 8 shows Dalianthe bibliographic University of coupling Technology, of Chinathe top countries/territories 127 1104 publishing 8.69 in the 18 NC. The 17countries are denotedUniversity by the of circular California node System, with USA different colors. 126The countries 1276 are 10.13 connected 16 by the 18link with differentKing Abdulazizwidths. More University, width Saudi shows Arabia the more intersection 120 1302 or references 10.85 of 21these countries,19 or the Hongdegree Kong of overlap Polytechnic between University, the reference Hong Kong lists, and thus 114 more 1350 coupling. 11.84 20 20 University of Chinese Academy Of Sciences CAS, China 106 729 6.88 16 21 South China University of Technology, China 99 624 6.30 13 22 Chongqing University, China 98 1194 12.18 19 23 National University of Singapore, Singapore 98 2691 27.46 20 24 Tianjin University, China 97 613 6.32 13 25 University of London, England 96 1005 10.47 20

4. Bibliographic Landscape This section demonstrates the disciplinary links with the help of bibliographic coupling. The VOSviewer visualization tool is used to show the coupling between the authors, countries and the institutions.

Publications 2018, 6, 32 12 of 22

We have first shown the bibliographic coupling between the authors in Figure7. The authors are connected by the link with different widths. More width shows more intersection or references of these authors, or degree of overlap between the reference lists, and thus more coupling. Moreover, each node representing the authors is also marked with three colors, red, blue and green. Each color consisting of group of countries forms a cluster which represents that these countries in a cluster have cited each other’s paper with more frequency. In the prominent cluster shown in red color, Wang J, Yang J, Yuan Y are the authors whose papers are more cited by the other authors in that cluster. The Other cluster is of a green color with authors such as: Zhang J, Wang D, Zhang HG, Zhang Y, and so on. Hammer B, Chen I, Wang GR, and so on form the third cluster of a blue color. We have mentioned the author’s name, with the last name followed by the initials of the first name as there are many Chinese authors whosePublications names match2018, 6, xclosely. FOR PEER REVIEW 12 of 21

Figure 7. Bibliographic coupling of the authors in NC. Figure 7. Bibliographic coupling of the authors in NC.

Figure8 shows the bibliographic coupling of the top countries/territories publishing in the NC. The countries are denoted by the circular node with different colors. The countries are connected by the link with different widths. More width shows the more intersection or references of these countries, or the degree of overlap between the reference lists, and thus more coupling. Two countries are said to be bibliographically coupled if there is intersection between the papers cited by them. Among all the countries, China is shown as the prominent one in Figure8 which also is the center node of the cluster between the countries such as Singapore, South Korea, India, Turkey, and Saudi Arabia. This suggest that these countries are most likely working on similar research areas and also referencing similar work. Among them, most probably all of other countries in this cluster are citing papers from China. The other visible clusters shown in red color are countries such as the USA, England, Japan, Germany, and France. Spain, Brazil, Italy, Canada, and Taiwan form the third cluster with a green color. In the last visualization of this section, we have shown the bibliographic coupling among several institutions as shown in Figure9. The Chinese Academy of sciences is the prominent node (institution) in the red cluster. Other institute in this clusters are Xidian University, National University of Singapore,

Figure 8. Bibliographic coupling of the countries publishing in NC.

Two countries are said to be bibliographically coupled if there is intersection between the papers cited by them. Among all the countries, China is shown as the prominent one in Figure 8 which also

Publications 2018, 6, x FOR PEER REVIEW 12 of 21

Publications 2018, 6, 32 13 of 22

Peking University, and so on. This strong bibliographic coupling for Chinese Academy of Sciences shows that most of the publications of other institutions in this cluster have cited its paper twice or more. The cluster with the blue color has universities such as South East University, Harbin institute of technology, King Abdulaziz University, and so on. Nanyang technology of university, Northeastern university, University of Granada,Figure 7. and Bibliographic so on form coupling the group of the authors of institutes in NC. in the third cluster.

Publications 2018, 6, x FOR PEER REVIEW 13 of 21

is the center node of the cluster between the countries such as Singapore, South Korea, India, Turkey, and Saudi Arabia. This suggest that these countries are most likely working on similar research areas and also referencing similar work. Among them, most probably all of other countries in this cluster are citing papers from China. The other visible clusters shown in red color are countries such as the USA, England, Japan, Germany, and France. Spain, Brazil, Italy, Canada, and Taiwan form the third cluster with a green color. In the last visualization of this section, we have shown the bibliographic coupling among several institutions as shown in Figure 9. The Chinese Academy of sciences is the prominent node (institution) in the red cluster. Other institute in this clusters are Xidian University, National University of Singapore, Peking University, and so on. This strong bibliographic coupling for Chinese Academy of Sciences shows that most of the publications of other institutions in this cluster have cited its paper twice or more. The cluster with the blue color has universities such as South East University, Harbin institute of technology, King Abdulaziz University, and so on. Nanyang technology of university,Figure Northeastern 8. Bibliographic university, coupling of Un theiversity countries of publishingGranada, inand NC. so on form the group Figure 8. Bibliographic coupling of the countries publishing in NC. of institutes in the third cluster. Two countries are said to be bibliographically coupled if there is intersection between the papers cited by them. Among all the countries, China is shown as the prominent one in Figure 8 which also

Figure 9. Bibliographic coupling of the institutions publishing in NC. Figure 9. Bibliographic coupling of the institutions publishing in NC. 5. Document Co-Citation Network This section uses citescape for visualization of NC publications. Citespace is an open source Java based application software used for visualizing trends from metadata of . Furthermore, document co-citation network analysis is studied along with the visualization of different document co-citation clusters and the references with strong citation bursts. The document co-citation network helps to explore the citation base of NC publications and can be seen in Figure 10. There are nodes and links in the given network. The nodes in Figure 10 represents the cited reference, the highly cited references are labeled with their first author and its year of publication. The size of a node is proportional to its number of citations count.

Publications 2018, 6, 32 14 of 22

5. Document Co-Citation Network This section uses citescape for visualization of NC publications. Citespace is an open source Java based application software used for visualizing trends from metadata of scientific literature. Furthermore, document co-citation network analysis is studied along with the visualization of different document co-citation clusters and the references with strong citation bursts. The document co-citation network helps to explore the citation base of NC publications and can be seen in Figure 10. There are nodes and links in the given network. The nodes in Figure 10 represents the cited reference, the highly cited references are labeled with their first author and its year ofPublications publication. 2018, 6 The, x FOR size PEER of aREVIEW node is proportional to its number of citations count. 14 of 21

Figure 10. Document co-citation analysis. Figure 10. Document co-citation analysis.

The top 10 most cited references by NC publications from various sources (Journals, conferences, etc.) Theare tabulated top 10 most in Table cited references6. The first byis the NC paper publications by Huang from et al. various [49] which sources proposes (Journals, a new conferences, learning etc.)approach are tabulated called the in extreme Table6. Thelearning first ismachine the paper (ELM) by Huang for regression et al. [ 49 and] which multiclass proposes classification. a new learning The approachpaper has calledbeen cited the extreme1283 times learning according machine to WoS. (ELM) The second for regression paper by and Huang multiclass et al. [42] classification. discusses Thethe papertheory has and been applications cited 1283 of times ELM. according Third ranked to WoS. is the The paper second by paperChang by etHuang al. [50] etwhich al. [42 proposes] discusses a thelibrary theory for andsupport applications vector machine of ELM. (SVM) Third rankedcalled LI isBSVM. the paper The byfourth Chang paper et al. is [ 50a survey] which on proposes ELM by a libraryHuang foret al. support [51]. It vectoris very machine interesting (SVM) to note called that LIBSVM. out of the The ten fourthtabulated paper articles is a survey six are onby ELMHuang, by Huangout of which et al. [three51]. It are is veryco-authored interesting by Chen. to note that out of the ten tabulated articles six are by Huang, out of which three are co-authored by Chen. Table 6. Top 10 Highly Cited References in NC.

Rank Title Year Source Authors Extreme Learning Machine for Regression IEEE transactions on systems, Huang GB, Zhou H, Ding 1 2012 and Multiclass Classification man, and cybernetics X, Zhang R Extreme learning machine: Theory and Huang GB, Zhu QY, Siew 2 2006 Neurocomputing applications CK ACM Transactions on LIBSVM: A library for support vector 3 2011 Intelligent Systems and Chang CC, Lin CJ machines Technology International Journal of Hung GB, Wang DH, Lan 4 Extreme learning machines: a survey 2011 Machine Learning and Y Cybernetics Letters: Convex incremental extreme 5 2007 Neurocomputing Huang GB, Chen L learning machine Enhanced random search based 6 2008 Neurocomputing Huang GB, Chen L incremental extreme learning machine Universal Approximation Using IEEE TRANSACTIONS ON Huang GB, Chen L, Siew 7 Incremental Constructive Feedforward 2006 NEURAL NETWORKS CK Networks With Random Hidden Nodes Graph Embedding and Extensions: A IEEE Transactions on Pattern Yan SC, Xu D, Zhang B, 8 General Framework for Dimensionality 2007 Analysis and Machine Zhang HJ, Yang Q, Lin S Reduction Intelligence IEEE Transactions on Pattern Felzenszwalb PF, Object detection with discriminatively 9 2010 Analysis and Machine Girshick RB, McAllester trained part-based models Intelligence D, Ramanan D.

Publications 2018, 6, 32 15 of 22

Table 6. Top 10 Highly Cited References in NC.

Rank Title Year Source Authors Extreme Learning Machine for Regression IEEE transactions on systems, Huang GB, Zhou H, 1 2012 and Multiclass Classification man, and cybernetics Ding X, Zhang R Extreme learning machine: Theory Huang GB, Zhu QY, 2 2006 Neurocomputing and applications Siew CK LIBSVM: A library for support ACM Transactions on Intelligent 3 2011 Chang CC, Lin CJ vector machines Systems and Technology International Journal of Machine Hung GB, Wang DH, 4 Extreme learning machines: a survey 2011 Learning and Cybernetics Lan Y Letters: Convex incremental extreme 5 2007 Neurocomputing Huang GB, Chen L learning machine Enhanced random search based 6 2008 Neurocomputing Huang GB, Chen L incremental extreme learning machine Universal Approximation Using IEEE TRANSACTIONS ON Huang GB, Chen L, 7 Incremental Constructive Feedforward 2006 NEURAL NETWORKS Siew CK Networks With Random Hidden Nodes Graph Embedding and Extensions: IEEE Transactions on Pattern Yan SC, Xu D, Zhang B, 8 A General Framework for 2007 Analysis and Zhang HJ, Yang Q, Lin S Dimensionality Reduction Machine Intelligence IEEE Transactions on Pattern Felzenszwalb PF, Girshick Object detection with discriminatively 9 2010 Analysis and RB, McAllester D, trained part-based models Machine Intelligence Ramanan D. Miche Y, Sorjamaa A, OP-ELM: Optimally Pruned Extreme IEEE Transactions on 10 2010 Bas P, Simula O, Jutten C, Learning Machine Neural Networks Lendasse A

5.1. Cluster Detection and Analysis The research patterns and emerging trends of NC publications are explored using CiteSpace. Figure 11 displays the document co-citation clusters of NC publications. The title term of each cluster is extracted from the title of the articles cited by NC publications using three methods namely log-likelihood test (LLR), mutual information test (MI), and term-frequency and inverse-document frequency (TF-IDF). For the NC publications there are a total of 187 clusters out of which the top 10 are as in the Table7 along with the top terms from each of the above-mentioned methods LLI, MI, and TF-IDF. Thereby, if considering LLR labels, then all the members of cluster 0 have spiking neurons as a common research theme and cluster 1 has neural networks in common. This can further be seen graphically in Figure 11 where all the clusters are shown. A total of 91 citing documents form the cluster ID 1 and are labelled neural networks by LLR. It is a fairly young cluster. With a total of 84 citing documents, cluster ID 2 is formed and is labelled Theoretical Results by LLR. Cluster ID 3 is formed of 76 citing documents and is labelled as Face Recognition by LLR. Cluster naming in Figure 11 has been done using LLR, which visualizes these clusters over the document co-citation analysis network. From Figure 11 and Table7 it is quite evident that the three largest clusters are spiking neurons, neural networks, and face recognition. Neural networks is the oldest cluster and Class is the youngest cluster. The minimum value of silhouettes is 0.889 which shows good clustering. The largest cluster 0 is of size 97 and a silhouette of 0.919 and is labeled spiking neurons by LLR. Figure 12 shows the timeline view of the document co-citations of top 24 largest clusters. All of these clusters are plotted in a horizontal manner. Further, it depicts the sequence of features of every cluster, which includes citation patterns and relationships among the other clusters. The temporal spread of all the cluster can be seen in Figure 12. Spiking neuron was a top research interest from 1992 to 2005 whereas neural networks was from 2002 to 2014. Moreover, a strong connection can be seen between the neural network cluster and convolution neural networks which is also a very obvious relationship. The temporal span of convolutional neural network as shown in Figure 12 is from 2008 and continues until the year 2017. Publications 2018, 6, 32 16 of 22 Publications 2018, 6, x FOR PEER REVIEW 16 of 21 Publications 2018, 6, x FOR PEER REVIEW 16 of 21

Figure 11. Document co-citations clusters. FigureFigure 11. 11.Document Document co-cit co-citationsations clusters. clusters.

Figure 12. Timeline view of the document co-citations. Figure 12. Timeline view of the document co-citations. Figure 12. Timeline view of the document co-citations.

Publications 2018, 6, 32 17 of 22

Table 7. Top 12 clusters in NC publications.

Cluster ID Size Silhouette Label (TFIDF) Label (LLR) Label (MI) Mean (Year) 0 89 0.919 Spiking neurons Spiking neurons Nonlinear interactions 1998 1 91 0.938 Neural networks Neural networks Nonlinear interactions 2007 2 84 0.889 Neural networks Theoretical results Nonlinear interactions 1990 3 76 0.916 Face recognition Face recognition Nonlinear interactions 2007 Independent Independent 4 74 0.944 Nonlinear interactions 1997 component analysis component analysis 5 59 0.948 Neural networks Conjugate Gradient Nonlinear interactions 1991 6 49 0.954 Neural networks Periodic solution Nonlinear interactions 2003 7 47 0.934 Neural networks Kernel method Nonlinear interactions 1999 Spatio-temporal 8 42 0.929 Visual cortex Nonlinear interactions 1995 receptive fields 9 40 0.891 Computational model Weakly electric fish Nonlinear interactions 1996 Extreme learning Extreme learning 10 37 0.982 Nonlinear interactions 2008 machine machine 11 36 0.977 Class Fuzzy systems Nonlinear interactions 2013 Publications 2018, 6, x FOR PEER REVIEW 17 of 21 Publications 2018, 6, x FOR PEER REVIEW 17 of 21 Publications 2018, 6, x FOR PEER REVIEW 17 of 21 Publications 2018, 6, x FOR PEER REVIEW 17 of 21 5.2. 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Neural, Table V,8. PTop 30 ReferencesYear1991 Strength15.3824 with theBegin 1994 Strongest End1999 Citation▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Bursts. 1992–2018 HertzHornik J, K,1991, 1989, Intro NeuralReferences Theory Networks, Neural, Table V2, V,8. P TopP359 30 ReferencesYear19911989 Strength15.382412.3758 with theBegin 1994 Strongest 19991997End Citation▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Bursts. 1992–2018 ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Hornik K, 1989,HornikHertz Neural J, K, 1991, 1989, Networks, Intro NeuralReferences Theory Networks, V2, Neural, Table P359 V2,V, 8. P P359Top1989 30 References1989Year1991 12.3758 Strength12.375815.3824 with 1994 theBegin 1994 Strongest 199719971999End Citation▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ Bursts.1992–2018 Hornik K, 1989, Neural Networks, V2, P359 1989 12.3758 1994 1997 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ Hertz J, 1991, IntroReferences Theory Neural, Table V,8. 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HertzHaykinHornik J, K,1991,S, 1994,1989, Intro NeuralNeuralReferences Theory NetworksNetworks, Neural, Comp, V2,V, P P359 V, Year19911989 Strength15.382412.3758 Begin 1994 1994 1999End1997 ▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂1992–2018 Haykin S, 1994, Neural NetworksTable Comp, 8. Top V, 30 References1994 10.6235 with the 1996 Strongest 2002 Citation▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ Bursts. HornikHertz J, K,1991, 1989, Intro NeuralReferences Theory Networks, Neural, V2,V, P P359 199419891991Year Strength10.623512.375815.3824 Begin 19961994 200219971999End ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂1992–2018 HaykinP S, 1994, Neural Networks Comp, V, ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ Haykin S, 1994,HertzHornik Neural J, K,1991, 1989, Networks Intro Neural Theory Networks, Comp, Neural, V2,V, P P359 19911989 15.382412.3758 1994 19991997 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ P References Year1994 Strength10.6235 Begin 1996 2002End ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂1992–2018 HornikHertzHaykin J, K,1991,S, 1989,1994, Intro Neural Theory Networks,Networks Neural, Comp, V2,V, P P3591994 V, 19891991 10.6235 12.375815.3824 1996 1994 200219971999 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ V, P HaykinHornikCybenkoP K,S, G, 1994,1989, 1989, NeuralReferences Mathematics NetworksNetworks, Of Comp, Control,V2, P359 V, 1989Year1994 Strength12.375810.6235 Begin 1994 1996 19972002End ▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂1992–2018 CybenkoHornikHertz J, K, 1991, G, 1989, 1989, Intro Neural Mathematics Theory Networks, Neural, Of Control, V2,V, P P359 19891991 12.375815.38248.6326 19941996 1994 19971999 ▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ HornikHaykinP K,S, 1994,1989, Neural NetworksNetworks, Comp, V2, P359 V, 19941989 10.623512.3758 19961994 20021997 ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Hertz J, 1991, Intro Theory Neural, V, P 19891991 15.38248.6326 19961994 19971999 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ PHaykinHornikCybenkoAnd Systems K,S, G, 1994,1989, 1989, Neural Mathematics NetworksNetworks, Of Comp, Control,V2, P359 V, 19891994 12.375810.6235 19941996 19972002 ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ And Systems 19941989 10.62358.6326 1996 20021997 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ Cybenko G, 1989,HaykinHornikPHertzCybenko Mathematics J, K, S,1991, G,1994,1989, 1989, Intro Neural Mathematics Theory Of NetworksNetworks, Control, Neural, Of Comp, V2,Control,V, P P359 V, 19891991 12.375815.3824 1994 19971999 ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ CybenkoPHaykinRumelhartAnd Systems S, G, 1994, De, 1989, 1986, Neural Mathematics Parallel Networks Distributed, Of Comp,Control, V, 19941989 10.62358.6326 1996 1996 20021997 ▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ HaykinHornik K,S, 1994,1989, NeuralNeural NetworksNetworks, Comp, V2, P3591989 V, 1989 8.6326 12.3758 1996 1994 19971997 ▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ RumelhartPHaykinCybenkoAnd Systems S, G, 1994, De, 1989, 1986, Neural Mathematics Parallel Networks Distributed, Of Comp, Control, V, 198919941986 10.62358.63267.5576 19961993 199720021994 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ And Systems 1994 10.6235 1996 2002 ▂▂▂▂▂▂▂▂ Hornik K, 1989, Neural Networks, V2, P359 1989 12.3758 1994 1997 ▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ AndCybenkoHaykinPRumelhartV1, P318 Systems S, G, 1994, De, 1989, 1986, Neural Mathematics Parallel Networks Distributed, Of Comp,Control, V, 198619941989 10.62357.55768.6326 19931996 199420021997 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂

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Rumelhart De, 1986, Parallel Distributed, ▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ WaibelMemory,V1,KohonenP328P183 P A, V,Teuvo, 1989, P Ieee 1989, T AcoustSelf Org Speech, Ass V37, 19891989 5.30895.2033 1996 1994 19971997 ▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ Rumelhart De, 1986, Parallel Distributed, 1986 5.4868 1992 1994 ▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Rumelhart De,Memory,KohonenWaibelJacobsP328 1986, Ra, A, Parallel V,Teuvo, 1989,1988, P IeeeNeural 1989, Distributed, T AcoustSelf Networks, Org Speech, Ass V1, V37,P295 19891988 5.20335.30894.6453 19941996 19971996 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ V1, P 1986 5.4868 1992 1994 ▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ JacobsP328WaibelKohonenMemory,Rumelhart Ra, A, Teuvo,V, 1989,1988, De, P 1986, IeeeNeural 1989, TParallel AcoustSelf Networks, Org Distributed, Speech, Ass V1, V37,P2951986 19881989 5.4868 4.64535.30895.2033 1992 19941996 199419961997 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ V1, P ▂▂▂▂▂▂▂▂ V1, P WaibelMemory,P328JacobsKohonen Ra, A, V,Teuvo, 1989,1988, P IeeeNeural 1989, T AcoustSelf Networks, Org Speech, Ass V1, V37,P295 198919881986 5.20335.30894.64535.4868 19941996 1992 199719961994 ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ P328WaibelV1,Jacobs P Ra,A, 1989,1988, IeeeNeural T Acoust Networks, Speech, V1, V37, P295 19891988 5.20334.6453 1994 1994 19971996 ▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ Memory,Waibel A, V, 1989, P Ieee T Acoust Speech, V37, 1989 5.3089 1996 1997 ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ KohonenXu L, 1994, Teuvo, Neural 1989, Networks, Self Org V7, Ass P609 1994 4.3694 1995 1998 ▂▂▂▂▂▂▂▂ JacobsP328 Ra, 1988, Neural Networks, V1, P295 19881989 4.64535.2033 1994 19961997 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ WaibelMemory, A, V,1989, P Ieee T Acoust Speech, V37, 1989 5.2033 1994 1997 ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Xu L, 1994, Neural Networks, V7, P609 19941989 4.36945.3089 19951996 19981997 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ P328Kohonen Teuvo, 1989, Self Org Ass ▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Jacobs Ra, 1988, Neural Networks, V1, P295 19891988 5.20334.6453 1994 19971996 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ Kohonen Teuvo,WaibelP328 1989, A, Self1989, OrgIeee T Ass Acoust Speech, V37, ▂▂▂▂▂▂▂▂ Memory,Xu L, 1994, V, Neural P Networks, V7, P609 1994 4.3694 1995 1998 ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ JacobsP328 Ra, 1988, Neural Networks, V1, P295198919881989 5.3089 4.64535.20335.3089 1996 1994 1996 199719961997 ▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ Waibel A, 1989, Ieee T Acoust Speech, V37, ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BishopMemory,Xu L, 1994, C, V,1991, Neural P Neural Networks, Computation, V7, P609 V3, 1994 4.3694 1995 1998 ▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ JacobsP328 Ra, 1988, Neural Networks, V1, P295 1988 4.6453 1994 1996 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Memory, V, P BishopXuWaibelJacobs L, 1994, Ra,C,A, 1991, 1989,1988, Neural Neural IeeeNeural Networks, T Acoust Computation, Networks, V7,Speech, P609 V1, V3, V37, P295 1994198819911989 4.36944.64534.01495.2033 19951994 1994 199819961997 ▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ JacobsP328 Ra, 1988, Neural Networks, V1, P295 19911988 4.01494.6453 19951994 1996 ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BishopP579 C, 1991, Neural Computation, V3, 1989 5.2033 1994 1997 ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ XuWaibel L, 1994, A, 1989, Neural Ieee Networks, T Acoust V7,Speech, P609 V37, 1994 4.3694 1995 1998 ▂▂▂▂▂▂▂▂ Jacobs Ra, 1988, Neural Networks, V1, P295 1988 4.6453 1994 1996 ▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ P579P328XuBishop L, 1994, C, 1991, Neural Neural Networks, Computation, V7, P609 V3, 199419911989 4.36944.01495.2033 1995 1994 199819961997 ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂

Jacobs Ra, 1988, Neural Networks, V1, P295 1988 4.6453 1994 1996 ▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂

BishopXuBaumP579P328 L, 1994, E C, B, 1991, 1989, Neural Neural Neural Networks, Computation, Computation, V7, P609 V3, V1, 19941991 4.36944.0149 1995 1995 19981996 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂

▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BaumXuJacobs L, 1994,E Ra, B, 1989,1988, Neural Neural Networks, Computation,Networks, V7, P609 V1, V1, P295 199419891988 4.36943.98064.6453 19951996 1994 199819971996 ▂▂▂▂▂▂▂▂ XuBishopP579 L, 1994, C, 1991, Neural Neural Networks, Computation, V7, P609 V3, 19911994 4.01494.3694 1995 19961998 ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂

▂▂▂▂▂▂▂▂ Jacobs Ra, 1988, Neural Networks, V1, P295 19891988 3.98064.6453 19961994 19971996 ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ P579BishopXuBaumP151 L, 1994, E C, B, 1991, 1989, Neural Neural Neural Networks, Computation, Computation, V7, P609 V3, V1, 19941991 4.36944.0149 1995 19981996 ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂

P151 19911989 4.01493.9806 19951996 19961997 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ BishopXuP579JacobsBaum L, 1994, E Ra,C, B, 1991, 1988, 1989,Neural Neural Neural Networks, Computation, Networks,Computation, V7, P609 V1, V3, V1, P295 19941988 4.36944.6453 19951994 19981996 ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ BaumP579BishopP151 E C, B, 1991, 1989, Neural Neural Computation, Computation, V3, V1, 19911989 4.01493.9806 1995 1996 19961997 ▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ BishopXu L, 1994, C, 1991, Neural Neural Networks, Computation, V7, P609 V3, 1994 4.3694 1995 1998 ▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ P579BishopBaumRitterP151 H,E C, B, 1992, 1991, 1989, Neural Neural Neural Computation Computation, Computation, S, V3, V1,V, P 198919911992 3.98064.01493.7901 19961995 199719961998 ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ 1991 4.0149 1995 1996 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ RitterXuP579 L, 1994,H, 1992, Neural Neural Networks, Computation V7, P609 S, V, P 19921994 3.79014.3694 19961995 1998 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ P151BaumBishopP579 E C, B, 1991, 1989, Neural Neural Computation, Computation, V3, V1, 19911989 4.01493.9806 19951996 19961997 ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂

Ritter H, 1992, Neural Computation S, V, P 1992 3.7901 1996 1998 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ BaumP579P151XuBishop L, 1994,E C, B, 1991,1989, Neural NeuralNeural Networks, Computation,Computation, V7, P609 V3,V1, 198919911994 3.98064.01494.3694 19961995 199719961998 ▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Baum E B, 1989, Neural Computation, V1, ▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ P151Ritter H, 1992, Neural Computation S, V, P 19891992 3.98063.7901 1996 1996 19971998 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ BishopP579BaumBattiti ER, C, B, 1992, 1991, 1989, Neural Neural Neural Comput, Computation, Computation, V4, P141 V3, V1, 19921991 3.54044.0149 19941995 19991996 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ RitterP151 H, 1992, Neural Computation S, V, P 19921989 3.79013.9806 1996 19981997 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ BaumP579 E B, 1989, Neural Computation, V1, 1989 3.9806 1996 1997 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BattitiP151Bishop R, C, 1992, 1991, Neural Neural Comput, Computation, V4, P141 V3, 19921991 3.54044.0149 19941995 19991996 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Ritter H, 1992, Neural Computation S, V, P 1992 3.7901 1996 1998 ▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ 1989 3.9806 1996 1997 P579BaumP151Battiti ER, B, 1992, 1989, Neural Neural Comput, Computation, V4, P141 V1, 1992 3.5404 1994 1999 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ RitterP151 H, 1992, Neural Computation S, V, P 199219891991 3.79013.98064.0149 1996 1995 199819971996 ▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ BarnardP579BattitiBaum ER, B,E, 1992, 1989,1992, Neural NeuralIeee T Comput, Neural Computation, Networ, V4, P141 V1, V3, 1992 3.5404 1994 1999 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ RitterP151 H, 1992, Neural Computation S, V, P 1992 3.7901 1996 1998 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ BarnardBattitiBaumRitter H,ER, B,E, 1992, 1989,1992, Neural NeuralIeee T Comput,Computation Neural Computation, Networ, V4, P141 S, V1,V, V3, P 19921989 3.54043.79013.9806 19941996 1996 199919981997 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ RitterP151 H, 1992, Neural Computation S, V, P 1992 3.54043.7901 19941996 19991998 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ BattitiBarnardP232Baum ER, B, E,1992, 1989,1992, Neural NeuralIeee T Comput, Neural Computation, Networ, V4, P141 V1, V3, 19891992 3.98063.5404 19961994 19971999 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ Ritter H, 1992, Neural Computation S, V, P 1992 3.7901 1996 1998 ▂▂▂▂▂▂▂▂ P232P151BattitiBarnard R, E,1992, 1992, Neural Ieee T Comput, Neural Networ, V4, P141 V3, 19921989 3.54043.9806 1994 1996 19991997 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Ritter H, 1992, Neural Computation S, V, P 1992 3.7901 1996 1998 ▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂

BarnardBattitiKuP232P151 Cc, R, 1995, E, 1992, 1992, Ieee Neural Ieee T Neural T Comput, Neural Networ, Networ, V4, P141V6, V3, 19921992 3.54043.5404 1994 1994 19991999 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ KuBattitiRitter Cc, H, R,1995, 1992, Ieee Neural T Neural Comput,Computation Networ, V4, V6,P141 S, V, P 199219951992 3.54043.79013.521 19941997 1996 199920001998 ▂▂▂▂▂▂▂▂ BattitiBarnardP232 R, E,1992, 1992, Neural Ieee T Comput, Neural Networ, V4, P141 V3, 1992 3.5404 1994 1999 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ Ritter H, 1992, Neural Computation S, V, P 19951992 3.79013.521 19971996 20001998 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ P232BarnardBattitiKuP144 Cc, R, 1995, E, 1992, 1992, Ieee Neural Ieee T Neural T Comput, Neural Networ, Networ, V4, P141V6, V3, 1992 3.5404 1994 1999 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ P144 19921995 3.54043.521 19941997 19992000 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂ BarnardBattitiP232RitterKu Cc, H,R, 1995, E, 1992, 1992, Ieee Neural Ieee T Neural T Comput,Computation Neural Networ, Networ, V4, P141 V6,S, V, V3, P 1992 3.54043.7901 19941996 19991998 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ KuP232BarnardP144 Cc, 1995, E, 1992, Ieee Ieee T Neural T Neural Networ, Networ, V6, V3, 19921995 3.54043.521 1994 1997 19992000 ▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Barnard E, 1992, Ieee T Neural Networ, V3, Battiti R, 1992, Neural Comput, V4, P141 1992 3.5404 1994 1999 ▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ P232BarnardKuRumelhartP144 Cc, 1995, E, 1992,De, Ieee 1986, Ieee T Neural Nature,T Neural Networ, V323, Networ, P533 V6, V3, 199519921986 3.54043.43253.521 199719941993 200019991994 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂ 1992 3.5404 1994 1999

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Rumelhart De, 1986, Nature, V323, P533 1986 3.4325 1993 1994 ▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ KuP232P144BattitiBarnard Cc, R,1995, E,1992, 1992, Ieee Neural IeeeT Neural T Comput, Neural Networ, Networ, V4, V6,P141 V3, 19951992 3.54043.521 19971994 20001999 ▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ KuDrago Cc, Gp,1995, 1992, Ieee Ieee T Neural T Neural Networ, Networ, V6, V3, ▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂ P144P232KuRumelhart Cc, 1995, De, Ieee 1986, T Neural Nature, Networ, V323, P533V6, 199519861992 3.43253.54043.521 1997 19931994 200019941999 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂ Barnard E, 1992, Ieee T Neural Networ, V3, ▂▂▂▂▂▂▂▂ DragoRumelhartP144 Gp, 1992,De, 1986, Ieee Nature,T Neural V323, Networ, P533 V3, 198619951992 3.43253.41913.521 199319971996 19942000 ▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Ku Cc, 1995, Ieee T Neural Networ, V6, 1995 3.521 1997 2000 ▂▂▂▂▂▂▂▂ P232 ▂▂▂▂▂▂▂▂ 1992 3.41913.5404 19961994 20001999 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ P144DragoP627Barnard Gp, E, 1992, Ieee T Neural Networ, V3, ▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Rumelhart De, 1986, Nature, V323, P533 19951986 3.43253.521 19971993 20001994 ▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂

KuP144 Cc, 1995, Ieee T Neural Networ, V6, ▂▂▂▂▂▂▂▂ P627P232 1992 3.4191 1996 2000 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂ RumelhartP144Drago Gp, De,1992, 1986, Ieee Nature, T Neural V323, Networ, P533 V3, 19861992 3.43253.5404 1993 1994 19941999 ▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂ 1995 3.521 1997 2000 ▂▂▂▂▂▂▂▂ Ku Cc, 1995, Ieee T Neural Networ, V6, ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ DragoP627P232 Gp, 1992, Ieee T Neural Networ, V3, 1992 3.4191 1996 2000 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Rumelhart De, 1986, Nature, V323, P533 1986 3.4325 1993 1994 ▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ P144 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂ 1995 3.521 1997 2000 KuRumelhartGirosi Cc, F,1995, 1995, De, Ieee Neural1986, T Neural Nature, Comput, Networ, V323, V7, P533P219V6, 19861995 3.43253.4191 19931996 19942000 ▂▂▂▂▂▂▂▂ DragoP627 Gp, 1992, Ieee T Neural Networ, V3, 1992 3.4191 1996 2000 ▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ RumelhartP144 De, 1986, Nature, V323, P533 1986 3.4325 1993 1994 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Girosi F, 1995, Neural Comput, V7, P219 1995 3.41913.521 19961997 2000 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂ P627DragoRumelhartKu Cc, Gp, 1995, 1992,De, Ieee 1986, Ieee T Neural Nature,T Neural Networ, V323, Networ, P533 V6, V3, 19861992 3.43253.4191 19931996 19942000 ▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂

P144 ▂▂▂▂▂▂▂▂ Girosi F, 1995, Neural Comput, V7, P219 19921995 3.41913.521 1996 1997 20002000 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ DragoRumelhartP627 Gp, 1992,De, 1986, Ieee Nature,T Neural V323, Networ, P533 V3, 1986 3.4325 1993 1994 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ P627DragoKohonenP144Girosi Gp,F, 1995,T, 1992, 1988, Neural Ieee Self T OrgComput, Neural Ass Networ,Memory, V7, P219 V3, V, 19921995 3.41913.4191 1996 1996 20002000 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ DragoRumelhart Gp, 1992,De, 1986, Ieee Nature,T Neural V323, Networ, P533 V3, 1986 3.4325 1993 1994 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ KohonenGirosiP627Drago F,Gp, 1995, T, 1992, 1988, Neural Ieee Self T OrgComput, Neural Ass Memory,Networ, V7, P219 V3, V, 199519921988 3.41913.3453 19961995 20001996 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂ 1992 3.4191 1996 2000 ▂▂▂▂▂▂▂▂ Rumelhart De, 1986, Nature, V323, P533 19881986 3.34533.4325 19951993 19961994 ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ P627KohonenP T, 1988, Self Org Ass Memory, V, ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂ DragoP627Girosi F,Gp, 1995, 1992, Neural Ieee T Comput, Neural Networ, V7, P219 V3, 19921995 3.4191 1996 2000 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂

P 1988 3.3453 1995 1996 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ GirosiP627RumelhartKohonenDrago F,Gp, 1995, T, 1992,De, 1988, Neural 1986, Ieee Self Nature,T Comput,Org Neural Ass V323, Networ,Memory, V7, P533P219 V3, V, 199519921986 3.41913.4325 1996 1993 20001994 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂ KohonenBroomheadP T, 1988,D S, 1988, Self OrgComplex Ass Memory, Systems, V, 1988 3.3453 1995 1996 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂ GirosiP627 F, 1995, Neural Comput, V7, P219 1995 3.4191 1996 2000 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BroomheadDragoGirosi F,Gp, 1995, 1992, D S,Neural Ieee1988, T Comput,Complex Neural Networ, V7,Systems, P219 V3, 199519881992 3.41913.34533.4191 19961995 1996 200019962000 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ KohonenP T, 1988, Self Org Ass Memory, V, 1988 3.3453 1995 1996 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ GirosiP627 F, 1995, Neural Comput, V7, P219 198819921995 3.34533.4191 19951996 19962000 ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ PKohonenGirosiBroomheadV2,Drago P321 F,Gp, 1995, T, 1992, 1988,D S,Neural Ieee1988, Self T OrgComput,Complex Neural Ass Memory,Networ, V7,Systems, P219 V3, V, 19951988 3.41913.3453 19961995 20001996 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ V2,P627 P321 19881992 3.34533.4191 1995 1996 19962000 ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ KohonenGirosiPBroomhead F, 1995, T, 1988,D NeuralS, 1988, Self Org Comput,Complex Ass Memory, V7, Systems, P219 V, 1995 3.4191 1996 2000 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂ BroomheadPKohonenV2,P627 P321 T, D1988, S, 1988, Self OrgComplex Ass Memory, Systems, V, 19881988 3.34533.3453 1995 1995 19961996 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Kohonen T, 1988, Self Org Ass Memory, V, ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂ PKohonenBroomheadV2,Girosi P321 F, 1995,T, 1988,D S,Neural 1988, Self OrgComput,Complex Ass Memory, V7,Systems, P219 V, 19881995 3.34533.4191 1995 1996 19962000 ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ 1988 3.3453 1995 1996 ▂▂▂▂▂▂▂▂ V2,GirosiBroomheadKohonenP P321 F, 1995, T, D1988, S,Neural 1988, Self OrgComput,Complex Ass Memory, V7,Systems, P219 V, 19951988 3.41913.3453 19961995 20001996 ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂ Girosi F, 1995, Neural Comput, V7, P219 19881995 3.34533.4191 1995 1996 19962000 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BroomheadPV2,Kohonen P321 T, D1988, S, 1988, Self OrgComplex Ass Memory, Systems, V, 1988 3.3453 1995 1996 ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ KohonenV2,PBroomhead P321 T, 1988,D S, 1988, Self OrgComplex Ass Memory, Systems, V, 19881988 3.34533.3453 1995 1995 19961996 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ V2,BroomheadP P321 D S, 1988, Complex Systems, 1988 3.3453 1995 1996 ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ V2,Kohonen P321 T, 1988, Self Org Ass Memory, V, 1988 3.3453 1995 1996 ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ PBroomhead D S, 1988, Complex Systems, 1988 3.3453 1995 1996 ▂▂▂▂▂▂▂▂ V2,PBroomhead P321 D S, 1988, Complex Systems, 1988 3.3453 1995 1996 ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BroomheadV2, P321 D S, 1988, Complex Systems, 1988 3.3453 1995 1996 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BroomheadV2, P321 D S, 1988, Complex Systems, 1988 3.3453 1995 1996 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ V2, P321 1988 3.3453 1995 1996 ▂▂▂▂▂▂▂▂ V2, P321 ▂▂▂▂▂▂▂▂

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PTop 30 References1991Year Strength15.3824 with theBegin 1994 Strongest 1999End Citation▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Bursts. 1992–2018 shownHertz J, in1991, Table IntroReferences Theory8. Neural, Table V,8. PTop 30 ReferencesYear1991 Strength15.3824 with theBegin 1994 Strongest 1999End Citation▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Bursts. 1992–2018 Hertz J, 1991, IntroReferences Theory Neural, Table V, 8. P Top 30 References1991Year Strength15.3824 with theBegin 1994 Strongest 1999End Citation▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ Bursts. 1992–2018 References Table 8. Top 30 ReferencesYear Strength with theBegin Strongest End Citation▂▂▂▂▂▂▂▂ Bursts. 1992–2018 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ HertzHornik J, K,1991, 1989, Intro Neural Theory Networks, Neural,Table V2, V,8. P TopP359 30 References19911989 15.382412.3758 with the 1994 Strongest 19991997 Citation▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Bursts. HornikHertz J, K,1991, 1989, Intro NeuralReferences Theory Networks, Neural, V2,V, P P359 Year19911989 Strength15.382412.3758 Begin 1994 19991997End ▂▂▂▂▂▂▂▂1992–2018 HertzHornik J, K,1991, 1989, Intro NeuralReferences Theory Networks, Neural, Table V2,V, 8. P P359Top 30 19911989YearReferences Strength15.382412.3758 with theBegin 1994 Strongest 19991997End Citation▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ Bursts.1992–2018 Table 8. Top 30 References with the Strongest Citation▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ Bursts. HornikHaykinHertz J, K,S,1991, 1989,1994, Intro NeuralReferences Theory Networks,Networks Neural, Comp, V2,V, P P359 V, 1989Year1991 Strength12.375815.3824 Begin 1994 19971999End ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂1992–2018 ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ HaykinHertzHornik J, K,S,1991, 1994,1989, Intro NeuralReferences Theory NetworksNetworks, Neural, Comp, V2,V, P P359 V, Year199119891994 Strength15.382412.375810.6235 Begin 19941996 199919972002End ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂1992–2018 HaykinHornikHertzP J, K,S,1991, 1994,1989, Intro NeuralReferences Theory NetworksNetworks, Neural, Comp, V2,V, P P359 V, 199419891991Year Strength10.623512.375815.3824 Begin 19961994 200219971999End ▂▂▂▂▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂1992–2018 HaykinHornikHertzP J, K,S,1991, 1994,1989, Intro NeuralReferences Theory NetworksNetworks, Neural, Comp, V2,V, P P359 V, 1989Year19941991 Strength12.375810.623515.3824 Begin 19941996 199720021999End 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PoggioV1, P318 T, 1990, P IEEE, V78, P1481 19901986 7.49547.5576 19951993 19981994 ▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ ChenRumelhartP302And Systems S, 1991, De, IEEE 1986, T ParallelNeural Networ,Distributed, V2, 1991 7.2449 1996 1999 ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ P302V1,ChenMoodyPoggioRumelhart P318 S, T, J,1991, 1989,1990, De, IEEE Neural1986,P IEEE, T NeuralParallel Computation, V78, P1481Networ, Distributed, V1,V2, 199119861990 7.24497.55767.4954 199619931995 199919941998 ▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂

▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ P302V1,MoodyChenRumelhartPoggio P318 S, T, J,1991, 1989,1990, De, IEEE 1986, NeuralP IEEE, T ParallelNeural Computation, V78, P1481Networ,Distributed, V1,V2, 1990199119891986 7.49547.24497.21937.5576 19951996 1993 1998199919971994 ▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ChenPoggioV1, P318 S, T,1991, 1990, IEEE P IEEE, T Neural V78, Networ,P1481 V2, 19861990 7.55767.4954 19931995 19941998 ▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ MoodyP302P281 J, 1989, Neural Computation, V1, 19891991 7.21937.2449 19951996 19971999 ▂▂▂▂▂▂▂▂ ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ PoggioChen S, T, 1991, 1990, IEEE P IEEE, T Neural V78, P1481Networ, V2, 19911990 7.24497.4954 19961995 19991998 ▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ MoodyP281P302V1, P318 J, 1989, Neural Computation, V1, 1989 7.2193 1995 1997 ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ P302P281PoggioChenMoody S, T, J,1991, 1989,1990, IEEE NeuralP IEEE, T Neural Computation, V78, P1481Networ, V1,V2, 198919901991 7.21937.49547.2449 19951996 199719981999 ▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂

▂▂▂▂▂▂▂▂ ▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ P281MoodyChenPaoP302Poggio Y-H, S, J,T,1991, 1989, 1990, IEEE AdaptiveNeural P IEEE, T Neural Computation, V78, Pattern Networ,P1481 Rec, V1,V2,V, P 199119891990 7.24497.21937.15737.4954 199619951994 1995 199919971998 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ ▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ MoodyChenPaoPoggioP302P281 Y-H, S, T, J,1991, 1989,1990, IEEE AdaptiveNeuralP IEEE, T Neural Computation, V78, Pattern P1481Networ, Rec, V1,V2,V, P 198919901991 7.15737.21937.49547.2449 199419951996 199719981999 ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ PaoChenP281P302Moody Y-H, S, J,1991, 1989, IEEE AdaptiveNeural T Neural Computation, Pattern Networ, Rec, V1,V2,V, P 19891991 7.21937.24497.1573 199519961994 19971999 ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ PaoP281P302MoodyChen Y-H, S, J, 1991, 1989, IEEE AdaptiveNeural T Neural Computation, Pattern Networ, Rec, V1, V,V2, P 19891991 7.15737.24497.2193 199419961995 19971999 ▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂

P302Moody J, 1989, Neural Computation, V1, 19891991 7.21937.2449 1995 1996 19971999 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ ChenPaoKohonenP281 Y-H, S, 1991, 1989,T, 1990, IEEE Adaptive P TIEEE, Neural Pattern V78, Networ, P1464 Rec, V2,V, P 19891990 7.15736.3202 19941996 19971998 ▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ MoodyKohonenPaoP281P302 Y-H, J, 1989,T, 1990, AdaptiveNeural P IEEE, Computation, Pattern V78, P1464 Rec, V1,V, P 199019891991 6.32027.15737.24497.2193 199619941995 199819971999 ▂▂▂▂▂▂▂▂

PaoKohonenMoodyP302P281 Y-H, J, 1989,T, 1990, AdaptiveNeural P IEEE, Computation, Pattern V78, P1464 Rec, V1,V, P 19891990 7.15736.32027.2193 199419961995 19971998 ▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂

▂▂▂▂▂▂▂▂ KohonenP281FahlmanPaoMoody Y-H, J, ST, 1989,1989, E, 1990, 1990, AdaptiveNeural P AdvIEEE, Computation, Neural PatternV78, P1464 Informati, Rec, V,V1, P 19901989 6.32027.21937.1573 199619951994 19981997 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ P281FahlmanMoodyPaoKohonen Y-H, J, S1989,T, E, 1990, 1990, AdaptiveNeural P AdvIEEE, Computation, Neural Pattern V78, P1464 Informati, Rec, V1,V, P 198919901989 7.15736.32027.2193 19941996 1995 199719981997 ▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ FahlmanKohonenPaoV2,P281 P524Y-H, S1989,T, E, 1990, 1990, Adaptive P AdvIEEE, Neural Pattern V78, P1464 Informati, Rec, V, P 19901989 6.32027.21937.1573 199619951994 19981997 ▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ FahlmanKohonenPaoV2,P281 P524Y-H, S1989,T, E, 1990, 1990, Adaptive P Adv IEEE, Neural Pattern V78, P1464 Informati, Rec, V, P 19901989 6.32027.1573 19961994 19981997 ▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ V2,PaoFahlmanNarendraKohonen P524Y-H, S1989,T, K E, 1990,S, 1990, 1990Adaptive P Adv,IEEE, Ieee Neural TransPattern V78, P1464Neural Informati, Rec, V, P 19901989 6.32027.1573 19961994 19981997 ▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ Publications 2018V2,NarendraFahlmanKohonenPao6 P524 Y-H, S T,1989,K E, 1990,S, 1990, 1990 Adaptive P Adv,IEEE, Ieee Neural Trans PatternV78, P1464Neural Informati, Rec, V, P 19901989 6.32026.24457.1573 19961995 1994 19981997 ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ FahlmanKohonen, , 32 ST, E, 1990, 1990, P Adv IEEE, Neural V78, P1464 Informati, 1990 6.3202 1996 1998 ▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ 18 of 22 NarendraPaoV2,Netw, P524Y-H, V1, 1989, KP4 S, 1990Adaptive, Ieee TransPattern Neural Rec, V, P 19901989 6.24456.32027.1573 199519961994 19981997 ▂▂▂▂▂▂▂▂ KohonenFahlman ST, E, 1990, 1990, P AdvIEEE, Neural V78, P1464 Informati, 1990 6.3202 1996 1998 ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ NarendraNetw,V2, P524 V1, KP4 S, 1990, Ieee Trans Neural 1990 6.2445 1995 1998 ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ V2,Netw,KohonenFahlmanNarendraFunahashi P524 V1, ST, K P4K,E, 1990,S, 1990,1989, 1990 P Adv,NeuralIEEE, Ieee Neural Trans V78, Networks, P1464 NeuralInformati, V2, 1990 6.24456.3202 19951996 1998 ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ Netw,FunahashiNarendraFahlmanV2,Kohonen P524 V1, S T,KP4 K,E, S,1990, 1990,1989, 1990 P ,AdvNeural IEEE,Ieee Neural Trans V78, Networks, NeuralP1464 Informati, V2, 199019891990 6.32026.24455.85446.3202 199619951994 1996 199819971998 ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ NarendraFahlmanKohonenV2, P524 S T,K E, 1990,S, 1990, 1990 P Adv,IEEE, Ieee Neural Trans V78, P1464Neural Informati, 1990 6.3202 1996 1998 ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ FunahashiNetw,P183 V1, P4K, 1989, Neural Networks, V2, 19891990 5.85446.2445 19941995 19971998 ▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ FahlmanV2,Narendra P524 S K E, S, 1990, 1990 Adv, Ieee Neural Trans NeuralInformati, 1990 6.24456.3202 19951996 1998 ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ FunahashiP183Netw, V1, P4K, 1989, Neural Networks, V2, 1989 5.8544 1994 1997 ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ Netw,P183V2,NarendraFunahashiFahlman P524 V1, SK P4 K,E, S, 1989,1990, 1990 ,NeuralAdv Ieee NeuralTrans Networks, Neural Informati, V2, 19891990 5.85446.32026.2445 199419961995 19971998 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Rumelhart De, 1986, Parallel Distributed, ▂▂▂▂▂▂▂▂ ▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ P183V2,RumelhartFunahashiFahlmanNarendraNetw, P524 V1, S K P4K,E,De, S, 1990,1989, 19901986, Adv,Neural IeeeParallel Neural Trans Networks, Distributed, NeuralInformati, V2, Table199019891990 8. 6.24455.8544Cont6.3202 . 19951994 1996 199819971998 ▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ 1986 5.4868 1992 1994 ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ FunahashiRumelhartNarendraNetw,P183V2, P524 V1, K P4K,De, S, 1989, 19901986, ,Neural IeeeParallel Trans Networks, Distributed, Neural V2, 198619891990 5.48685.85446.32026.2445 1992199419961995 199419971998 ▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ V1, P ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ RumelhartNarendraV1,P183V2,Netw,Funahashi PP524 V1, K P4K,De, S, 1989, 19901986, ,Neural IeeeParallel Trans Networks, Distributed, Neural V2, 198919861990 5.85445.48686.2445 199419921995 199719941998 ▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ P183V1,Netw,FunahashiRumelhartNarendra P V1, P4KK,De, S, 1989, 1986,1990 Neural, ParallelIeee Trans Networks, Distributed, Neural V2, 198619901989 5.48686.24455.8544 199219951994 199419981997 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂

V1,Netw,RumelhartNarendraFunahashiP183Kohonen P V1, Teuvo,K P4K,De, S, 1989, 19901986, 1989, ,Neural IeeeParallel Self Trans Networks,Org Distributed, NeuralAss V2, 198919861990 5.85445.48686.2445 19941992 1995 199719941998 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂

Kohonen Teuvo, 1989, Self Org Ass 1989 5.3089 1996 1997 ▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ RumelhartKohonenFunahashiP183V1,Netw,References P V1, Teuvo, K,P4De, 1989, 1986, 1989, Neural Parallel Self Networks,Org Distributed, Ass V2, Year 198919861990 Strength 5.30895.48686.24455.8544Begin 1996199219951994 End199719941998 ▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ 1992–2018 Memory, V, P ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ KohonenFunahashiMemory,V1,Netw,P183Rumelhart P V1, Teuvo,V, P4K,De, P 1989, 1986, 1989, Neural Parallel Self OrgNetworks, Distributed, Ass V2, 19861989 5.48685.30895.8544 199219961994 19941997 ▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ V1,Memory,P183RumelhartKohonenFunahashi P V,Teuvo, De,K, P 1989, 1986, 1989, Neural Parallel Self OrgNetworks, Distributed, Ass V2, 19891986 5.30895.85445.4868 199619941992 19971994 ▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ Waibel A, 1989,Memory,P183KohonenFunahashiRumelhartV1,Waibel Ieee P A, T V,Teuvo, Acoust 1989,K,De, P 1989, 1986, Ieee 1989, Speech,Neural TParallel AcoustSelf Networks,Org Distributed, Speech, Ass V2,V37, 198619891989 5.48685.30895.8544 19921996 1994 199419971997 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ KohonenRumelhartWaibelV1,P183 P A, Teuvo, 1989, De, 1986, Ieee 1989, TParallel AcoustSelf Org Distributed, Speech, Ass V37,1989 19891986 5.2033 5.85445.48685.2033 1994 19941992 199719971994 ▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ WaibelMemory,P328 A, V, 1989, P Ieee T Acoust Speech, V37, 1989 5.20335.3089 19941996 1997 ▂▂▂▂▂▂▂▂ RumelhartP183V1,Kohonen P Teuvo, De, 1986, 1989, Parallel Self Org Distributed, Ass 19891986 5.30895.4868 19961992 19971994 ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ V37, P328 WaibelP328Memory, A, V, 1989, P Ieee T Acoust Speech, V37, 1989 5.2033 1994 1997 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Memory,V1,Rumelhart P V, De, P 1986, Parallel Distributed, 1986 5.4868 1992 1994 ▂▂▂▂▂▂▂▂ P328KohonenWaibel A, Teuvo, 1989, Ieee 1989, T AcoustSelf Org Speech, Ass V37, 1989 5.20335.3089 19941996 1997 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ P328V1,RumelhartKohonenMemory, P Teuvo,V, De, P 1986, 1989, Parallel Self Org Distributed, Ass 19891986 5.30895.4868 1996 1992 19971994 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ WaibelJacobs Ra, A, 1989,1988, IeeeNeural T Acoust Networks, Speech, V1, V37,P295 19891988 5.20334.6453 1994 19971996 ▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ WaibelKohonenMemory,V1, P A, Teuvo,V, 1989, P Ieee 1989, T AcoustSelf Org Speech, Ass V37, 19861989 5.48685.3089 19921996 19941997 ▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ JacobsP328 Ra, 1988, Neural Networks, V1, P295 19881989 4.64535.2033 1994 19961997 ▂▂▂▂▂▂▂▂ ▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ Jacobs Ra, 1988,JacobsKohonenP328V1,Memory,Waibel Neural P Ra, A, Teuvo,V, 1989,1988, Networks, P IeeeNeural 1989, T AcoustSelf Networks, V1, Org P295 Speech, Ass V1, V37,P2951988 19891988 4.6453 5.20334.64535.3089 1994 19941996 199619971996 ▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ JacobsP328Memory,WaibelKohonen Ra, A, V, Teuvo,1989,1988, P IeeeNeural 1989, T Acoust SelfNetworks, Org Speech, Ass V1, V37,P295 19881989 4.64535.30895.2033 19941996 19961997 ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂

▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ Memory,Waibel A, V, 1989, P Ieee T Acoust Speech, V37, 19891989 5.20335.3089 1994 1996 19971997 ▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ KohonenJacobsXuP328 L, 1994, Ra, Teuvo, 1988, Neural Neural 1989, Networks, Self Networks, Org V7, Ass P609 V1, P295 19881994 4.64534.3694 19941995 19961998 ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ WaibelXuJacobsP328Memory, L, 1994, Ra, A, V,1989,1988, Neural P IeeeNeural Networks, T Acoust Networks, V7,Speech, P609 V1, V37,P295 199419881989 4.36944.64535.30895.2033 199519941996 199819961997 ▂▂▂▂▂▂▂▂

JacobsXuWaibelMemory,P328 L, 1994, Ra, A, V, 1989,1988, Neural P IeeeNeural Networks, T Acoust Networks, V7,Speech, P609 V1, V37,P295 198819941989 4.64534.36945.2033 19941995 199619981997 ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂

Xu L, 1994, Neural Networks, V7, P609 1994 4.3694 1995 1998 ▂▂▂▂▂▂▂▂ XuP328BishopJacobsWaibel L, 1994, Ra, C,A, 1991,1988, 1989,Neural NeuralNeuralIeee Networks, T Acoust Computation,Networks, V7, Speech, P609 V1, V3, P295V37, 199419891988 4.36945.20334.6453 19951994 199819971996 ▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ P328BishopWaibelJacobsXu L, 1994, Ra,C,A, 1991,1989,1988, Neural NeuralIeeeNeural Networks, T Acoust Computation,Networks, Speech,V7, P609 V1, V3, V37,P295 1988199419911989 4.64534.36944.01495.2033 19941995 1994 199619981997 ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Jacobs Ra, 1988, Neural Networks, V1, P295 1988 4.6453 1994 1996 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BishopXuP579P328 L, 1994, C, 1991, Neural Neural Networks, Computation, V7, P609 V3, 199119941989 4.01494.36945.2033 19951994 199619981997 ▂▂▂▂▂▂▂▂

▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ Xu L, 1994, Neural Networks, V7, P609 1994 4.3694 1995 1998 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Bishop C, 1991,BishopJacobsP579P328 Neural Ra,C, 1991, 1988, Computation, Neural Neural Computation, Networks, V1, V3, P295 19911988 4.01494.6453 19951994 1996 ▂▂▂▂▂▂▂▂

▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ P579JacobsBishopBaumXu L, 1994, ERa, C, B, 1991, 1988,1989, Neural Neural NeuralNeural Networks, Computation, Networks,Computation, V7, P609 V1, V3, V1,P295 199119881994 4.01494.64534.3694 19951994 19961998 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ 1991 4.0149 1995 1996 ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ P579BaumBishopXuJacobs L, 1994,E Ra,C, B, 1991, 1989, 1988,Neural Neural Neural Networks, Computation, Computation,Networks, V7, P609 V1, V3, V1, P295 1994199119891988 4.36944.01493.98064.6453 19951996 1994 1998199619971996 ▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ V3, P579 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BishopXu L, 1994, C, 1991, Neural Neural Networks, Computation, V7, P609 V3, 1994 4.3694 1995 1998 ▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BaumJacobsP579P151 ERa, B, 1988,1989, Neural Networks,Computation, V1, V1,P295 198919911988 3.98064.01494.6453 199619951994 19971996 ▂▂▂▂▂▂▂▂ ▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ XuBishop L, 1994, C, 1991, Neural Neural Networks, Computation, V7, P609 V3, 19911994 4.01494.3694 1995 19961998 ▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BaumP151P579 E B, 1989, Neural Computation, V1, 1989 3.9806 1996 1997 ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ P579P151XuBishopBaum L, 1994, E C, B, 1991, 1989, Neural Neural Neural Networks, Computation, Computation, V7, P609 V3, V1, 198919941991 3.98064.36944.0149 19961995 199719981996 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂

▂▂▂▂▂▂▂▂ Baum E B, 1989, Neural Computation, ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ P151BaumBishopRitterP579Xu L, H,1994,E C, B, 1992, 1991, 1989, Neural Neural Neural Neural Networks, Computation Computation, Computation, V7, P609 S, V3, V1,V, P 1991198919921994 4.01493.98063.79014.3694 19951996 1995 1996199719981998 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ ▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BaumBishopRitterXuP579P151 L, 1994,H,E C, B, 1992, 1991, 1989, Neural Neural Neural Neural Networks, Computation Computation, Computation, V7, P609 S, V3, V1,V,1989 P 1992198919941991 3.9806 3.79013.98064.36944.0149 1996 19961995 1997199819971996 ▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ V1, P151 RitterBishopP151P579Baum H,E C, B, 1992, 1991, 1989, Neural Neural Neural Computation Computation, Computation, S, V3, V,V1, P 198919921991 3.98063.79014.0149 19961995 199719981996 ▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ RitterP151P579BaumBishop H,E C,B, 1992, 1991,1989, Neural Neural Computation Computation, S, V3,V,V1, P 199219911989 3.79014.01493.9806 19961995 199819961997 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂

P579Baum E B, 1989, Neural Computation, V1, 19891991 3.98064.0149 1996 1995 19971996 ▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ BishopRitterBattitiP151 H,R, C, 1992, 1991, Neural Neural ComputationComput, Computation, V4, P141 S, V3, V, P 1992 3.79013.5404 19961994 19981999 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BaumBattitiRitterP151P579 H,ER, B, 1992, 1989, Neural Neural ComputationComput, Computation, V4, P141 S, V1,V, P 199219911989 3.54043.79014.01493.9806 199419961995 1999199819961997 ▂▂▂▂▂▂▂▂

Ritter H, 1992,RitterBattitiBaumP579P151 Neural H,ER, B, 1992,Computation 1989, Neural Neural ComputationComput, Computation, S, V, V4, P P141S, V1,V, 1992 P 19921989 3.7901 3.79013.54043.9806 1996 19961994 1998199819991997 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂

▂▂▂▂▂▂▂▂ BattitiP151BarnardRitterBaum H, R,E E,B,1992, 1992,1989, Neural IeeeNeural T Comput,Computation Neural Computation, Networ, V4, P141 S, V,V1, V3, P 19921989 3.54043.98063.7901 19941996 199919971998 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ P151BarnardBaumRitterBattiti H,ER, B, E,1992, 1989,1992, Neural NeuralIeee T ComputationComput, Neural Computation, Networ, V4, P141S, V1,V, V3, P 19921989 3.79013.54043.9806 19961994 1996 199819991997 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Ritter H, 1992, Neural Computation S, V, P 1992 3.7901 1996 1998 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BarnardBattitiP232P151 R, E, 1992, 1992, Neural Ieee T Comput, Neural Networ, V4, P141 V3, 19921989 3.54043.9806 19941996 19991997 ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ Battiti R, 1992, Neural Comput, V4, P141 1992 3.5404 1994 1999 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Battiti R, 1992,BarnardRitterP232P151 Neural H, E, 1992, Comput, 1992, Neural Ieee T V4, Computation Neural P141 Networ, S, V, 1992V3, P 1992 3.5404 3.54043.7901 1994 19941996 199919991998 ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ P232RitterBarnardKuBattiti Cc, H,R, 1995, E,1992, 1992, Ieee Neural Ieee T Neural T ComputationComput, Neural Networ, Networ, V4, P141V6,S, V, V3, P 1992 3.54043.7901 19941996 19991998 ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂

▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ P232KuBarnardBattitiRitter Cc, R,H, 1995, E, 1992,1992, 1992, Ieee NeuralNeural Ieee T Neural T Comput, ComputationNeural Networ, Networ, V4, P141V6, S, V, V3, P 199219951992 3.54043.79013.521 19941997 1996 199920001998 ▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BarnardBattiti R, E, 1992, 1992, Neural Ieee T Comput, Neural Networ, V4, P141 V3, 1992 3.5404 1994 1999 ▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ KuRitterP232P144 Cc, H, 1995, 1992, Ieee Neural T Neural Computation Networ, V6,S, V, P 19951992 3.54043.79013.521 199719941996 200019991998 ▂▂▂▂▂▂▂▂ ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ BattitiBarnard R, E,1992, 1992, Neural Ieee T Comput, Neural Networ, V4, P141 V3, 1992 3.5404 1994 1999 ▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Barnard E, 1992,KuP144P232 IeeeCc, 1995, T Neural Ieee T Neural Networ, Networ, V6, 1995 3.521 1997 2000 ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ P232P144BattitiBarnardKu Cc, R, 1995, E,1992, 1992, Ieee Neural Ieee T Neural T Comput, Neural Networ, Networ, V4, P141V6,1992 V3, 19951992 3.5404 3.54043.521 1994 19971994 199920001999 ▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ P144BarnardP232Battiti R, E, 1992, 1992, Neural Ieee T Comput,Neural Networ, V4, P141 V3, 19921992 3.54043.5404 1994 1994 19991999 ▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ V3, P232 KuRumelhart Cc, 1995, De, Ieee 1986, T Neural Nature, Networ, V323, P533V6, 19951986 3.43253.521 19971993 20001994 ▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ KuBarnardP232 Cc, 1995, E, 1992, Ieee Ieee T Neural T Neural Networ, Networ, V6, V3, 1992 3.5404 1994 1999 ▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ RumelhartBattitiP144 R, 1992, De, Neural1986, Nature, Comput, V323, V4, P533P141 198619951992 3.43253.54043.521 199719931994 200019941999 ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ ▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂ RumelhartBarnardP144P232Ku Cc, 1995, E, 1992,De, Ieee 1986, Ieee T Neural Nature,T Neural Networ, V323, Networ, P533 V6, V3, 199519861992 3.43253.54043.521 199719931994 200019941999 ▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ RumelhartP144P232KuDragoBarnard Cc, Gp,1995, E, 1992, De,1992, Ieee 1986, Ieee IeeeT Neural Nature,T T Neural Neural Networ, V323, Networ, Networ, P533 V6, V3, V3, 198619921995 3.43253.54043.521 199319941997 199419992000 ▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ Ku Cc, 1995, Ieee T Neural Networ, ▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂ P232DragoBarnardRumelhartKuP144 Cc, Gp,1995, E, 1992,De, Ieee 1986, Ieee T Neural Nature,T Neural Networ, V323, Networ, P533 V6, V3, 1995198619921992 3.43253.41913.54043.521 199719931996 1994 200019941999 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂ 1995 3.521 1997 2000 ▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ KuP144P232 Cc, 1995, Ieee T Neural Networ, V6, 19921995 3.54043.521 19941997 19992000 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ DragoRumelhartP627 Gp, 1992,De, 1986, Ieee Nature,T Neural V323, Networ, P533 V3, 19921986 3.41913.4325 19961993 20001994 ▂▂▂▂▂▂▂▂

V6, P144 RumelhartP144 De, 1986, Nature, V323, P533 19861995 3.43253.521 19931997 19942000 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ DragoKuP627P232 Cc, Gp, 1995, 1992, Ieee Ieee T Neural T Neural Networ, Networ, V6, V3, 1992 3.4191 1996 2000 ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ P627P144DragoRumelhartKu Cc, Gp, 1995, 1992,De, Ieee 1986, Ieee T Neural Nature,T Neural Networ, V323, Networ, P533 V6, V3, 199219951986 3.41913.43253.521 199619971993 20001994 ▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂

P627P144Rumelhart De, 1986, Nature, V323, P533 19861995 3.43253.521 1993 1997 19942000 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ DragoKuGirosi Cc, F, Gp,1995, 1995, 1992, Ieee Neural Ieee T Neural T Comput, Neural Networ, Networ, V7, P219V6, V3, 19921995 3.4191 1996 2000 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂

▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ DragoRumelhart Gp, 1992,De, 1986, Ieee Nature,T Neural V323, Networ, P533 V3, 1986 3.4325 1993 1994 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Rumelhart De,GirosiP627P144 1986, F, Nature, 1995, Neural V323, Comput, P533 V7, P219 1986 19951992 3.4325 3.41913.521 1993 19971996 19942000 ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ ▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂ GirosiRumelhartP627P144Drago F,Gp, 1995, 1992,De, Neural1986, Ieee Nature,T Comput, Neural V323, Networ, V7, P533P219 V3, 199219951986 3.41913.4325 19961993 20001994 ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂ GirosiP627RumelhartDragoKohonen F,Gp, 1995, T, 1992,De, 1988, Neural 1986, Ieee Self Nature,T Comput,Org Neural Ass V323, Networ,Memory, V7, P533P219 V3, V, 199519861992 3.41913.4325 19961993 20001994 ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂ KohonenGirosiDragoP627Rumelhart F,Gp, 1995, T, 1992, De,1988, Neural 1986, Ieee Self TNature, OrgComput, Neural Ass V323, Networ,Memory, V7, P219P533 V3, V, 1992199519881986 3.41913.34533.4325 19961995 1993 200019961994 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Drago Gp, 1992,KohonenDragoGirosiRumelhartP627P Ieee F,Gp, T1995, T, Neural1992,De, 1988, Neural1986, Ieee SelfNetwor, Nature,T OrgComput, Neural Ass V323, Networ,Memory, V7, P533P219 V3, V, 1988199519861992 3.34533.41913.4325 199519961993 199620001994 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂

▂▂▂▂▂▂▂▂ KohonenGirosiDragoPP627 F,Gp, 1995, T, 1992, 1988, Neural Ieee Self T OrgComput, Neural Ass Memory,Networ, V7, P219 1992V3, V, 199519881992 3.4191 3.41913.3453 1996 19961995 200020001996 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂ V3, P627 PP627KohonenBroomheadGirosiDrago F, Gp, 1995, T, 1992, D1988, S,Neural 1988,Ieee Self T OrgComput,Complex Neural Ass Memory, Networ, V7,Systems, P219 V3,V, 198819921995 3.34533.4191 19951996 19962000 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂ ▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂ PP627BroomheadKohonenDragoGirosi F,Gp, 1995, T, 1992, D1988, S,Neural Ieee1988, Self T OrgComput,Complex Neural Ass Networ,Memory, V7,Systems, P219 V3, V, 199519881992 3.41913.34533.4191 19961995 1996 200019962000 ▂▂▂▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂

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Hornik K, 1990, Neural Networks, V3, P551 1990 3.3453 1995 1996 Hornik K, 1990, Neural Networks, V3, P551 1990 3.3453 1995 1996 ▂▂▂▂▂▂▂▂

The top rank is taken by hertz [52] with the highest burst strength of 15.3824. The reference is a The topbook rank titled is taken “Introduction by hertz to [52 the] withtheory the of neural highest computation” burst strength published of 15.3824. in the year The 1991. reference The second is a titled “Introductionarticle [53] proposed to the the theory concept of neural of multilayer computation” feed forward published networks in the as yearuniversal 1991. approximators. The second article [53] proposedThe articles the with concept the longest of multilayer citation burst feed is starti forwardng in 1996 networks after two as universalyears if its publication approximators. in 1994 The articlesand with ends the longestin the year. citation The burstbook is is titled starting “Neural in 1996 Networks: after two A yearsComprehensive if its publication Foundation” in 1994 and and ends inintroduces the year. the The concept book of is neural titled networks “Neural from Networks: an engineering A Comprehensive perspective. Foundation” and introduces the concept of neural networks from an engineering perspective. 6. Conclusions 6. Conclusions This paper presents an overall insight and the hidden structure of the journal of neurocomputing (NC) through an extensive bibliometric study on the publications and citations history from 1992– This paper presents an overall insight and the hidden structure of the journal of neurocomputing 2018. Over these years of its publications, NC has emerged as one of the most prominent journals. (NC) through an extensive bibliometric study on the publications and citations history from 1992–2018. Table 9 shows the comparison of NC with respect to the several other related journals with respect Over these yearsto the ofTP, its TC, publications, CPP, and IF. The NC analysis has emerged is performed as one over of the last most 10 years prominent of publications. journals. NC Table stands9 at shows the comparisonthe top in terms of NC of the with total respect number to of the publications several other with TP related of 7954 journals and ranks with at the respect 2nd spot to the in terms TP, TC, CPP, andof IF.total The citations. analysis With is performed respect to overthe IF, last NC 10 (3.317) years ofranks publications. above the Soft NC computing stands at the (2.472) top inand terms of theEngineering total number Applications of publications of Artificial with Intelligence TP of 7954 (2.894). and ranks at the 2nd spot in terms of total citations. With respect to the IF, NC (3.317) ranks above the Soft computing (2.472) and Engineering Applications of Artificial IntelligenceTable (2.894). 9. Comparison of NC with other related Journals. In this paper, we have exploredJournal the publication history Induction using Year bibliometric TP (10 Years) TC methods (10 Years) and CPP techniques. Impact Factor Information Sciences (INS) 1968 5125 102,308 20.0 4.832 It probed the publicationSoft structure Computing and(SC) the citation history1997 of the journal.1620 A14,096 total of 10,3088.7 papers2.472 were analyzed from 1992–2018,Knowledge-Based which Systems received (KBS) a total of1987 106,536 citations2092 (CPP31,516 = 10.33). 15.1 The highest4.529 Engineering Applications of Artificial Intelligence (EAAI) 1988 1617 22,107 13.7 2.894 publications cameIEEE in Transactions 2016, while on Fuzzy the Systems maximum (IEEE TFS) citations1993 were received1186 in 2015.42,013 There are35.4 16 papers7.651 Applied Soft Computing (ASOC) 2001 3581 56,584 15.8 3.541 which got more than 200Neurocomputing citations and (NC) the most cited paper 1989 came in 7954 2006, which 63,491 got 2762 7.98 citations 3.317

In this paper, we have explored the publication history using bibliometric methods and techniques. It probed the publication structure and the citation history of the journal. A total of 10,308 papers were analyzed from 1992–2018, which received a total of 106,536 citations (CPP = 10.33). The highest publications came in 2016, while the maximum citations were received in 2015. There are 16 papers which got more than 200 citations and the most cited paper came in 2006, which got 2762 citations to date. The most productive and influential author of NC is Cao. China and USA are the top two nations in publications of NC. Chinese Academy of Sciences, China published the highest number of papers. Interestingly, from the overall compilation of the bibliometric results, it is evident that China has played a prominent role in the publications and citations structure of the NC. However, one thing to be noted is that there may be certain limitations in the study because of the focus being on the data retrieved from the WoS database. The limitations include the coverage of research publications wherein WoS omits certain research articles, a smaller database of journals which leads to under-reported statistics, and so on. However, the data quality in WoS is tremendous and is thus the most acknowledged source of almost all scientometric studies. Therefore, the major aim of this paper is to reveal the current status of the NC publications by utilizing the results in WoS [54,55]. The other limitation of this study arises from the fact that it is from the metric level, that is, it deals with the number of citations and papers. However, numbers do represent quantity but citations do not represent the quality. Another limitation is that this study creates a profile of the NC journal rather than the area itself, although it can be claimed that NC has been the pioneer in leading the research in this field. Certain limitations arise due to data retrieved from WoS as not all journals are indexed with WoS and thus an array of citations are missed. A comparison with other indexing platforms such as Scopus, can be a good future scope of this study. In conclusion, the journal of NC has played an important role in shaping academic research since its inception. NC is certainly discovering and developing trends in the domain of soft computing.

Publications 2018, 6, 32 19 of 22 to date. The most productive and influential author of NC is Cao. China and USA are the top two nations in publications of NC. Chinese Academy of Sciences, China published the highest number of papers. Interestingly, from the overall compilation of the bibliometric results, it is evident that China has played a prominent role in the publications and citations structure of the NC.

Table 9. Comparison of NC with other related Journals.

Journal Induction Year TP (10 Years) TC (10 Years) CPP Impact Factor Information Sciences (INS) 1968 5125 102,308 20.0 4.832 Soft Computing (SC) 1997 1620 14,096 8.7 2.472 Knowledge-Based Systems (KBS) 1987 2092 31,516 15.1 4.529 Engineering Applications of 1988 1617 22,107 13.7 2.894 Artificial Intelligence (EAAI) IEEE Transactions on Fuzzy 1993 1186 42,013 35.4 7.651 Systems (IEEE TFS) Applied Soft Computing (ASOC) 2001 3581 56,584 15.8 3.541 Neurocomputing (NC) 1989 7954 63,491 7.98 3.317

However, one thing to be noted is that there may be certain limitations in the study because of the focus being on the data retrieved from the WoS database. The limitations include the coverage of research publications wherein WoS omits certain research articles, a smaller database of journals which leads to under-reported statistics, and so on. However, the data quality in WoS is tremendous and is thus the most acknowledged source of almost all scientometric studies. Therefore, the major aim of this paper is to reveal the current status of the NC publications by utilizing the results in WoS [54,55]. The other limitation of this study arises from the fact that it is from the metric level, that is, it deals with the number of citations and papers. However, numbers do represent quantity but citations do not represent the quality. Another limitation is that this study creates a profile of the NC journal rather than the area itself, although it can be claimed that NC has been the pioneer in leading the research in this field. Certain limitations arise due to data retrieved from WoS as not all journals are indexed with WoS and thus an array of citations are missed. A comparison with other indexing platforms such as Scopus, Google Scholar can be a good future scope of this study. In conclusion, the journal of NC has played an important role in shaping academic research since its inception. NC is certainly discovering and developing trends in the domain of soft computing.

Author Contributions: Conceptualization: P.K.M.; Methodology: A.K.S., Data Curation, M.J.; Writing—Original Draft Preparation: M.J.; Writing—Review & Editing: M.J., A.K.S., P.K.M. and A.A.; Supervision: P.K.M. and A.A. Funding: This research received no external funding. Acknowledgments: The authors gratefully acknowledge the valuable comments received from the editors and the reviewers, which has helped them in improving the paper significantly. Conflicts of Interest: The authors declare no conflict of interest.

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