DOCSLIB.ORG

The Analysis of Possibilities of Modern Neural Network

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Analysis of Possibilities of Modern Neural Network CSITA AUTOMATION ISSN 2414-9055 UDC 004.9:622.1:622.271 https://doi.org/10.31721/2414-9055.2016.2.3.4 THE ANALYSIS OF POSSIBILITIES OF MODERN NEURAL NETWORK SIMULATOR SOFTWARE FOR REALIZATION OF LOCAL INTELLECTUAL REGULATORS Kupin A., Sc.D, Professor, Mysko B., PhD student Kryvyi Rih National University Abstract. A research objective is the analysis of possibilities of the software of modern stimulators of neural networks. By application of methods of the system analysis the best packages on the basis of twelve criteria are revealed. The gained results can be applied for implementation of local intellectual regulators. Keywords: neural networks, software simulators, algorithms of learning. Introduction. Now in the world a rather big structures for local intellectual regulators of the type amount of powerful neural network simulators is [9, 10] the methodology [4, 7] was used. On the first worked out [1]. The main differences between them plan the criteria, related to simplicity of the use of consist in an amount of neuron architectures, neural packages, evidence of presentation of topologies and methods of supported studies, information and possibilities of the use of typical limitations in relation to filling of network, presence neuron structures, criteria of optimization and of programmatic interface with widely spoken algorithms of studies of neural networks were put in languages or programming (as MS Visual C++, the forefront. Unlike the work [4], it was appraised Delphi, C++ Builder, C#, etc.), environments for and taken into account the cost of licenses of organization of data exchange and possibility of software of all packages. integration into own software projects. Except universality a neural package must Thus, most known and powerful are such be simple in the use, have intuitively clear interface software projects [2-6]: and provide evidence of presentation of - NeuralWorks Pro II/Plus (Aspen information. On the basis of these requirements Technology, Inc.); such criteria of comparison are set forth: - Neuro Solutions (NeuroDimension, Inc.); - simplicity of creation and studies of neural - MATLAB Neural Network Toolbox network, intuitively clear interface; (MathWorks, Inc.); - simplicity of preparation of educational - STATISTICA Neural Networks (Statsoft, selection; Inc.); - the evidence and plenitude of - Brain-Maker Pro (California Scientific presentation of information in the process of Software, Inc.); creation and studies of neural networks; - NeuroLand (Institute of mathematical - amount of standard neuron paradigms, machines and systems, Ukraine). criteria and algorithms of studies of neural There is also an enormous amount of less networks; known, simplified or specialized packages (i.e. for - possibility of creation of original neuron supercomputers, clusters, GRID-calculations, etc.). structures; For example, Deductor Academic, JavaNNS, Neuro - possibility of the use of original criteria of Office, Neuro Pro, Neuro Shell, NNC, NNW, Sim optimization; Brain, T-System, Nimfa, SNNS, SNC (Software - possibility of the use of original algorithms Neuron Computer), etc. [8]. of studies of neural networks; Materials and Methods. With the aim of - possibility of programmatic expansions of analysis of the marked neural simulators and the neural packages; choice of the most suitable for application in the - cost of licenses, presence of trial version. process of programmatic realization of neural © Computer science, information technology, automation. 2016. Volume 2, issue 3 4 CSITA AUTOMATION ISSN 2414-9055 The estimation of neural packages on the practically any conceivable tuning of package under marked criteria was conducted by a ten-point scale. a task. Except adequate facilities of visualization this Research of authors was also taken into account in neural package is equipped with powerful quality end-point of testing of the above-mentioned neural facilities. packages [1-3]. On the basis of comparison such A neural network is designed as a set of the results are received. neurons connected together. The function of The NeuroSolutions is a universal neural activating a neuron can be selected from five package intended for design of a wide circle of standard functions (piece-linear, function of a sign artificial neural networks. Basic dignity of the and three types of sigmoid) and also set in an marked neural package consists in its flexibility: optional kind by user. except traditional of neural networks paradigms (as The connections between neurons are set full coherent multi-layered neural networks or a optionally on the stage of planning of neural self-organizing map of Kohonen) a neural package network, here they can be simply enough changed contains powerful editor of the visual planning of in the process of work with a neural network. A neural network that allows creating practically any neural package supports all known types of own neuron structures and algorithms of their connections: lines cross and reverse. The neural studies. Especially it should be noted that this neural package of NeuroSolutions also has rather powerful package allows the user to enter its own criteria of facilities for organization of educational selections. studies of neural network, not limiting to only The built-in converters of data support graphic widespread, but far from being optimal criteria of a images in BMP format, ordinary text files with minimum of a mean-square error. The neural numeric or symbol data and also the functions of package of NeuroSolutions is equipped by powerful continuous argument (for example, time), set in an and well carefully thought-out facilities of analytical kind or as a selection of values. visualization (it is possible to control practically all A neural package allows using the wide set the parameters, beginning from a neuron network of learning criteria – discrete and continuous (for structure and ending with a process and result of example, by use of integrating neurons). Besides, it’s studies). The presence of powerful facilities of possible to enter your own criteria. By studies it is visualization destroys a neural package on the level possible to use both a built-in back-propagation of CAD-systems. Thus NeuroSolutions can be algorithm or delta-rules, and your own. A correctly considered a valuable and all-sufficient planning built system of visualization of learning process system and design of neural networks. allows conducting the analysis of weight coefficients The package of NeuroSolutions is intended and their direct changing in the learning process for work in the operating systems and bringing in corresponding adjustments. By Windows'9x/NT/2000/XP/2003/Vista/7-10. Except means of neural package it is possible to enter noise the correctly organized facilities of co-operating description not only by testing of neural network, with the operating system (OLE2 is supported) a but also by its studies. neural package is also provided with a generator of For acceleration of work the neural package initial code and facilities that allow using the of NeuroSolutions contains the generator of external modules for planning and studies of neural standard architectures (Neural Wizard). By means of network. A package supports the programs, written this generator it is possible to set architecture of by means of the language C++ for the most known neural network quickly, pick up an educational compilers (Microsoft Visual C++ and Borland C++) selection, criteria and methods of studies. The most and also a program as an executable code (libraries known neural networks paradigms are supported: of DLL). Thus, the package of NeuroSolutions shows multi-layered networks, RBF, net of Kohonen, self- a flexible open system that can be complemented organizing structures and others. and modified, if necessary. There is a built-in macro A cost of base licenses of package of language in the package that allows doing NeuroSolutions according to the data [3] for all © Computer science, information technology, automation. 2016. Volume 2, issue 3 5 CSITA AUTOMATION ISSN 2414-9055 operating systems depends on their level. First level by users. The package has a built-in code generator (Educator) 195$ – mastering of MLP of neural nets. that supports the compiler of Microsoft Visual C++. Second level (Users) 495$ – recognition of static The cost of license of NeuralWorks patterns. Third level (Consultants) 995$ – Professional is from 1995 to 4995 $ depending on a recognition of dynamic patterns and prophecy. A platform (DOS, Windows, NT, Sun, RS6000, SGI). The fourth level (Professional) 1495$ – generation of professional variant (9995 - 14995 $) executed as a Visual Basic code for application in the software. The specialized environment of development allows to fifth level (Developers) 1995$ – includes initial generate the external С++ code and to use libraries on С++. The additional program programming with С++. Thus, here it is possible to (195 - 1495$ depending on a level) generates DLL- develop any new neural networks and it also includes libraries of neural networks created in additional packages for real-time applications NeuroSolution. together with fuzzy logic and genetic algorithms. Results. The final estimation of this package MATLAB + Neural Network Toolbox (NNT). It and other simulators was made by a ten-point scale. allows to rationally combine the possibilities of End-point received taking into account the data [7] powerful mathematical package and simultaneous are presented in tab. 1. work
Load more

Recommended publications
  • Neural Networks
    AIAI andand thethe NetNet •• Buyer’sBuyer’s GuideGuide 19.1 Where Intelligent Technology Meets the Real World www.pcai.com IntelligentIntelligent Applications Applications TThehe ModernModern RoboticRobotic Also:Also: “Movement”“Movement” Agents, Business Applications, TThehe StateState ofof AIAI Today:Today: TheThe Business Intelligence, WWeb:eb: AI’sAI’s NewNew PlaygroundPlayground Computational Intelligence, Data Analysis & Mining, Robotics:Robotics: RobotsRobots ThatThat Intelligent Applications, Intelligent Tools, MimicMimic AnimalsAnimals Intelligent Tutoring, Intelligent Web Searching, TThehe NationalNational ScienceScience Neural Networks, Foundation:Foundation: EncouragingEncouraging Robotics, tthehe ResearchersResearchers ofof Speech Recognition, TTomorrowomorrow Web Based Expert Systems, Plus:Plus: AIAI andand thethe Net,Net, Training, Bookzone,Bookzone, Buyer’sBuyer’s Guide,Guide, AI Conferences, ProductProduct UUpdatespdates and more! PC AI 2 19.1 Quantities Limited Buy PC AI Back Issues 1995 1999 A Great Resource 9 #1 Intelligent Tools 13 #1 Intelligent Tools & Languages 9 #2 Fuzzy Logic / Neural Networks (Knowledge Verification) for AI Research 9 #3 Object Oriented Development 13 #2 Rule and Object Oriented 9 #4 Knowledge-Based Systems Development (Data Mining) $8.00/Issue - US 9 #5 AI Languages 13 #3 Neural Nets & Fuzzy Logic (For Us and Canadian and 9 #6 Business Applications (Searching) Foreign Postage 13 #4 Knowledge-Based Systems contact PC AI or visit the 1996 (Fuzzy Logic) 10 #1 Intelligent Applications PC AI web site) 13 #5 Data Mining (Simulation and 10 #2 Object Oriented Development Modeling) Order online at 10 #3 Neural Networks / Fuzzy Logic 13 #6 Business Applications www.pcai.com 10 #4 Knowledge-Based Systems (Machine Learning) Total amount enclosed 10 #5 Genetic Algorithm & Modeling 10 #6 Business Applications 2000 $____________.
    [Show full text]
  • Artificial Neural Networks
    ARTIFICIAL NEURAL NETWORKS: A REVIEW OF TRAINING TOOLS Darío Baptista, Fernando Morgado-Dias Madeira Interactive Technologies Institute and Centro de Competências de Ciências Exactas e da Engenharia, Universidade da Madeira Campus da Penteada, 9000-039 Funchal, Madeira, Portugal. Tel:+351 291-705150/1, Fax: +351 291-705199 Abstract: Artificial Neural Networks became a common solution for a wide variety of problems in many fields. The most frequent solution for its implementation consists of building and training the Artificial Neural Network within a computer. For implementing a network in an efficient way, the user can access a large choice of software solutions either commercial or prototypes. Choosing the most convenient solution for the application according to the network architecture, training algorithm, operating system and price can be a complex task. This paper helps the Artificial Neural Network user by providing a large list of solution available and explaining their characteristics and terms of use. The paper is confined to reporting the software products that have been developed for Artificial Neural Networks. The features considered important for this kind of software to have in order to accommodate its users effectively are specified. The development of software that implements Artificial Neural is a rapidly growing field driven by strong research interests as well as urgent practical, economical and social needs. Copyright CONTROLO2012 Keywords: Artificial Neural Networks, Training Tools, Training Algorithms, Software. 1. INTRODUCTION commercialization of new ANN tools. With the purpose to inform which tools are available at present Nowadays, in different areas, it is important to and make the choice of which tool to use, this paper analyse nonlinear data to do prediction, classification contains the description of software that has been or to build models.
    [Show full text]
  • The Nnlib2 Library and Nnlib2rcpp R Package for Implementing Neural Networks
    The nnlib2 library and nnlib2Rcpp R package for implementing neural networks Vasilis N Nikolaidis1 1 University of Peloponnese DOI: 10.21105/joss.02876 Software • Review Summary • Repository • Archive Artificial Neural Networks (ANN or NN) are computing models used in various data-driven applications. Such systems typically consist of a large number of processing elements (or Editor: Kakia Chatsiou nodes), usually organized in layers, which exchange data via weighted connections. An ever- increasing number of different neural network models have been proposed and used. Among Reviewers: the several factors differentiating each model are the network topology, the processing and • @schnorr training methods in nodes and connections, and the sequences utilized for transferring data • @MohmedSoudy to, within and from the model etc. The software presented here is a C++ library of classes and templates for implementing neural network components and models and an R package Submitted: 22 October 2020 that allows users to instantiate and use such components from the R programming language. Published: 23 May 2021 License Authors of papers retain copyright and release the work Statement of need under a Creative Commons Attribution 4.0 International A significant number of capable, flexible, high performance tools for NN are available today, License (CC BY 4.0). including frameworks such as Tensorflow (Abadi et al., 2016) and Torch (Collobert et al., 2011), and related high level APIs including Keras (Chollet & others, 2015) and PyTorch (Paszke et al., 2019). Ready-to-use NN models are also provided by various machine learning platforms such as H2O (H2O.ai, 2020) or libraries, SNNS (Zell et al., 1994) and FANN (Nissen, 2003).
    [Show full text]
  • Neural Network FAQ, Part 1 of 7
    Neural Network FAQ, part 1 of 7: Introduction Archive-name: ai-faq/neural-nets/part1 Last-modified: 2002-05-17 URL: ftp://ftp.sas.com/pub/neural/FAQ.html Maintainer: [email protected] (Warren S. Sarle) Copyright 1997, 1998, 1999, 2000, 2001, 2002 by Warren S. Sarle, Cary, NC, USA. --------------------------------------------------------------- Additions, corrections, or improvements are always welcome. Anybody who is willing to contribute any information, please email me; if it is relevant, I will incorporate it. The monthly posting departs around the 28th of every month. --------------------------------------------------------------- This is the first of seven parts of a monthly posting to the Usenet newsgroup comp.ai.neural-nets (as well as comp.answers and news.answers, where it should be findable at any time). Its purpose is to provide basic information for individuals who are new to the field of neural networks or who are just beginning to read this group. It will help to avoid lengthy discussion of questions that often arise for beginners. SO, PLEASE, SEARCH THIS POSTING FIRST IF YOU HAVE A QUESTION and DON'T POST ANSWERS TO FAQs: POINT THE ASKER TO THIS POSTING The latest version of the FAQ is available as a hypertext document, readable by any WWW (World Wide Web) browser such as Netscape, under the URL: ftp://ftp.sas.com/pub/neural/FAQ.html. If you are reading the version of the FAQ posted in comp.ai.neural-nets, be sure to view it with a monospace font such as Courier. If you view it with a proportional font, tables and formulas will be mangled.
    [Show full text]
  • Forecasting with Artificial Neural Networks
    Forecasting with Artificial Neural Networks EVIC 2005 Tutorial Santiago de Chile, 15 December 2005 Æ slides on www.neural-forecasting.com Sven F. Crone Centre for Forecasting Department of Management Science Lancaster University Management School email: [email protected] EVIC’05 © Sven F. Crone - www.bis-lab.com Lancaster University Management School? EVIC’05 © Sven F. Crone - www.bis-lab.com What you can expect from this session … Simple back propagation algorithm [Rumelhart et al. 1982] ∂C(t pj ,o pj ) E p = C(t pj , o pj ) o pj = f j (net pj ) Δ p w ji ∝ − ∂w ji ∂C(t ,o ) ∂C(t ,o ) ∂net pj pj = pj pj pj ∂w ji ∂net pj ∂w ji ∂C(t ,o ) Æ „How to …“ on Neural δ = − pj pj pj ∂net pj Network Forecasting ∂C(t ,o ) ∂C(t ,o ) ∂o δ = − pj pj = pj pj pj pj with limited maths! ∂net pj ∂opj ∂net pj ∂o pt ' = f j (net pj ) ∂net pj ∂C(t pj ,opj ) ' δ pj = f j (net pj ) Æ CD-Start-Up Kit for ∂o pj ∂ w o ∂C(t ,o ) ∂net ∂C(t ,o ) ∑ ki pi Neural Net Forecasting pj pj pk = pj pj i ∑ ∂net ∂o ∑ ∂net ∂o k pk pj k pk pj Æ 20+ software simulators ∂C(t pj ,opj ) = ∑ wkj = − ∑δ pj wkj k ∂net pk k Æ datasets ' δ pj = f j (net pj )∑δ pj wkj Æ literature & faq k ⎧∂C(t pj ,opj ) ' ⎪ f j (net pj ) if unit j is in the output layer ⎪ ∂opj Æ slides, data & additional info on δ pj = ⎨ ⎪ ' f j (net pj )∑δ pk wpjk if unit j is in a hidden layer www.neural-forecasting.com ⎩⎪ k EVIC’05 © Sven F.
    [Show full text]
  • Uhm Ms 3812 R.Pdf
    UNIVERSITY OF HAWAI'I LIBRARY ADVANCED MARINE VEIDCLE PRODUCTS DATABASE -A PRELIMINARY DESIGN TOOL A THESIS SUBMITTED TO THE GRADUATE DMSION OF THE UNIVERSITY OF HAWAI'I IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN OCEAN ENGINEERING AUGUST 2003 By Kristen A.L.G. Woo Thesis Committee: Kwok Fai Cheung, ChaiIperson Hans-Jurgen Krock John C. Wiltshire ACKNOWLEDGEMENT I would like to thank my advisor Prof. Kwok Fai Cheung and the other committee members Prof. Hans-Jiirgen Krock and Dr. John Wiltshire for the time and effort they spent with me on this project. I would also like to thank the MHPCC staff, in particular, Mr. Scott Splean for their advice and comments on the advanced-marine-vehicle products database. Thanks are also due to Drs. Woei-Min Lin and JUll Li of SAIC for their advice on the neural network and preliminary ship design tools. I would also like to thank Mr. Yann Douyere for his help with MatLab. The work described in this thesis is a subset of the project "Environment for Design of Advanced Marine Vehicles and Operations Research" supported by the Office of Naval Research, Grant No. NOOOI4-02-1-0903. iii ABSTRACT The term advanced marine vehicle encompasses a broad category of ship designs typically referring to multihull ships such as catamarans, trimarans and SWATH (small waterplane area twin hull) ships, but also includes hovercrafts, SES (surface effect ships), hydrofoils, and advanced monohulls. This study develops an early stage design tool for advanced marine vehicles that provides principal particulars and additional parameters such as fuel capacity and propulsive power based on input ship requirements.
    [Show full text]
  • Neural Networks in Mobile Robot Motion, Pp
    Janglová, D. / Neural Networks in Mobile Robot Motion, pp. 15-22, Inernational Journal of Advanced Robotic Systems, Volume 1 Number 1 (2004), ISSN 1729-8806 Neural Networks in Mobile Robot Motion Danica Janglová Institute of Informatics SAS, [email protected] Abstract: This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the “free” space using ultrasound range finder data. The second neural network “finds” a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented. Keywords: Mobile Robot, Neural Network, Ultrasound Range Finder, Path Planning, Navigation 1. Introduction initial location and orientation, a goal location and orientation, and a set of obstacles located in workspace, Over the last few years, a number of studies were the problem is to find a continuous path from the initial reported concerning a machine learning, and how it has position to the goal position, which avoids collisions with been applied to help mobile robots to improve their obstacles along the way. In other words, the motion operational capabilities.
    [Show full text]
  • Comparative Economic Forecasting with Neural Networks: Forecasting Aggregate Business Sales from S&P 500 and Interest Rates
    Pace University School of Computer Science and Information Systems Department of Computer Science Eshwar Singh Comparative Economic Forecasting with Neural Networks: Forecasting Aggregate Business Sales from S&P 500 and Interest Rates Master’s Thesis Supervising Faculty: Dr Anthony Joseph. Abstract Research utilizing neural networks is a rapidly growing field of study for its extensive analytical behavior. This study uses neural networks to forecast economic time-series data. It focuses on comparative economic forecasting using neural networks with the objective of forecasting aggregate business sales using Standard and Poor’s (S&P) 500 index and interest rates. The application software used was Mathworks’ Matlab and NeuroDimension’s NeuroSolutions. The two networks used were time-lagged feedforward backpropagation multilayer perceptron and the Elman recurrent neural network. These neural network models were implemented, and then trained and tested on sales and S&P 500 index, sales and interest rates, as well as sales and S&P 500 index together with interest rates. In particular, various data manipulation procedures were used, software tools employed during preprocessing, different methodologies applied during forecasting, and error metrics techniques applied during post-processing analysis and data evaluation. Furthermore, the study showed that current stock market prices were correlated to past stock prices suggesting that stock market data have long memory and can be useful for forecasting purposes. This is contrary to the efficient market hypothesis and the random walk theory assumption, which states that today’s asset price, does not depend on previous prices. Moreover, the Matlab and NeuroSolutions neural network modeling frameworks were compared to determine their relative performance and suitability for economic time series forecasting.
    [Show full text]
  • Artificial Neural Networks Modelling for Monitoring and Performance Analysis of a Heat and Power Plant
    ISRN LUTMDN/TMHP—06/5084—SE ISSN 0282 - 1990 Artificial Neural Networks Modelling for Monitoring and Performance Analysis of a Heat and Power Plant Mehrzad Kaiadi Thesis for the Degree of Master of Science Division of Thermal Power Engineering Department of Energy Sciences LUND UNIVERSITY Faculty of Engineering LTH P.O. Box 118, S – 221 00 Lund Sweden Artificial Neural Networks Modelling for Monitoring and Performance Analysis of a Heat and Power Plant Mehrzad Kaiadi January 2006 Master Thesis Division of Thermal Power Engineering Department of Energy Sciences Lund University, Sweden Box 118 SE-221 00 LUND http://www.vok.lth.se © Mehrzad Kaiadi 2006 ISSN 0282 - 1990 ISRN LUTMDN/TMHP—06/5084–SE Printed in Sweden Lund 2006 To my family!!! ii Preface This master thesis has been carried out at the Department of Energy Science at (LTH), at Lund University. The study was carried out in cooperation with Öresundskraft, Västhamnsverket AB in Helsingborg, during fall and winter of 2005. I would like to express my gratitude to all the people who has helped me to carry out this work. Thanks to my supervisors, Professor Tord Torisson, Associate Professor Mohsen Assadi and Dr. Jaime Arriagada for their precious guidance throughout this work. Special thanks to Ulrik Snygg, my contact person at Västhamnsverket, for providing me all information I needed. I would also like to dedicate a word of thanks to Associate Professor Sudipta De for his precious guidance and his sincerely cooperation during this work. Thanks to Dr. Azra Selimovic for answering all my questions. Thanks to Magnus Fast for the long and useful discussions about Artificial Neural Networks.
    [Show full text]
  • Available Toolboxes
    Center of Excellence on Soft Computing and Intelligent Information Processing A Report on Available toolboxes In artificial intelligence Version 1.0: 2015/5/20 Presented by: Nasrin Hossein-Nia,Fatemeh Anbari Faculty Advisor: M. Akbarzadeh NUMERIC BENCHMARK FUNCTIONS Research Objective and Methodology In this Research, we collect a number of useful toolboxes and sofwares that can be used in artificial intelligence works. MATLAB is an important software that have many useful toolboxes for different 2engineering fields in particular artificial intelligence, so we decided to collect a number of useful toolboxes in MATLAB and other software. First we found a list of all toolboxes in MATLAB, then we separated those that were related to artificial intelligence field and found them in the internet. We search another available toolboxes, too. Finally, we categorized them into 10 sections as follows: Section 1: Fuzzy toolboxes,48 Section 2: Neural network toolboxes,28 Section 3: Optimization toolboxes,14 Section 4: Image and signal processing toolboxes,5 Section 5: Classification & Clustering toolboxes,16 Section 6: Machine learning toolboxes,11 Section 7: Data mining toolboxes,9 Section 8: Pattern recognition toolboxes,3 Section 9: Matrix computations,4 Section 10: Other useful toolboxes,80 Each section includes a number of toolboxes .A short description about each toolbox and its link is provided. Clearly, this research doesn’t include all of available toolboxes but can be a good guide and we hope it will be helpful to students. CENTER OF EXCELLENCE ON SOFT COMPUTING AND INTELLIGENT INFORMATION PROCESSING PAGE 2 NUMERIC BENCHMARK FUNCTIONS Outline section 1: Fuzzy toolboxes ................................................................................ 4 section 2: Neural network toolboxes .............................................................
    [Show full text]
  • Application of Neural Networks to Data Mining
    SQU Journal For Science, 12 (2) (2007)121-141 © 2007 Sultan Qaboos University Application of Neural Networks to Data Mining Zlatinka Svetoslavova Kovacheva Higher College of Technology, Al Khuwair, Muscat, Oman,E-mail: [email protected] ﺗطﺑﯾﻘﺎت اﻟﺷﺑﻛﺎت اﻟﻌﺻﺑﯾﺔ ﻓﻲ اﻟﺗﻧﻘﯾب ﻋن اﻟﺑﯾﺎﻧﺎت ذﻻﺗﯾﻧﻛﺎ ﺳﻔﯾﺗوﺳﻼﻓوﻓﺎ ﻛوﻓﺎﺗﺷﯾﻔﺎ ﺧﻼﺻﺔ : اﻟﺗﻧﻘﯾب ﻓﻲ اﻟﺑﯾﺎﻧﺎت ھو ﻋﻠم ﻣﮭم ﻻﺳﺗﺧﻼص اﻟﻣﻌﻠوﻣﺎت ﻣن اﻟﺑﯾﺎﻧﺎت واﻷرﻗﺎم اﻟﺗﻲ ﻓﻲ اﻷﻏﻠب ﻻ ﺗﻛون ﻣﻔﮭوﻣﺔ ﻟﻣﻌظم اﻟﻧﺎس ﺑﺣﯾث ﯾﺗم اﺳﺗﺧﻼص اﻟﻣﻌﻠوﻣﺎت وﺗﻘدﯾﻣﮭﺎ ﺑﻠﻐﺔ ﺳﮭﻠﺔ وﻣﻔﮭوﻣﺔ,ﻛﻣﺎ ﯾﻘوم ﺗﻧﻘﯾب اﻟﺑﯾﺎﻧﺎت ﺑﺎﺳﺗﺧدام اﻟﺑﯾﺎﻧﺎت اﻟﺣﺎﻟﯾﺔ اﻟﻣﺗوﻓرة ﻹﺳﺗﻧﺑﺎط ﺑﯾﺎﻧﺎت ﺟدﯾدة ﺗﺳﺗﺧدم ﻟﻌﻣل اﻟﺗوﻗﻌﺎت ﺣول ﺣﻘل ﻣﻌﯾن. اﻟﺷﺑﻛﺎت اﻟﻌﺻﺑﯾﺔ أﯾﺿﺎ ﺗﺳﺗﺧدم ﻛﺄداة ﺗﺣﻠﯾل ﺗﻛون ﻣﺻﻣﻣﺔ ﻟﺑﻧﺎء اﻟﻧﻣﺎذج اﻟﺗﻔﺳﯾرﯾﺔ وﻓﻲ اﻟوﻗت اﻟﺣﺎﻟﻲ ﺗوﺟد ﺷﺑﻛﺎت ﻋﺻﺑﯾﺔ ﺗﻘوم ﺑﺎﺳﺗﺧدام ﻋﻣﻠﯾﺎت ﺣﺳﺎﺑﯾﺔ ﻣﻌﻘدة ﺑﺷﻛل ﻣﺑﺎﺷر ﻟﺗﺳﺎھم ﻓﻲ ﻋﻣﻠﯾﺔ ﺑﻧﺎء اﻟﻧﻣﺎذج اﻟﺗﻔﺳﯾرﯾﺔ ﺣﯾث أن اﺧر ﺗطورات اﻟﺑﺣث ﻓﻲ اﻟﺷﺑﻛﺎت اﻟﻌﺻﺑﯾﺔ ﺗﺟﻌﻠﮭﺎ ﻗرﯾﺑﺔ ﺟدا ﻣن ﺗﻌرﯾف اﻟﺗﻧﻘﯾب ﻓﻲ اﻟﺑﯾﺎﻧﺎت ﻓﮭﻲ أﯾﺿﺎ ﺗﻘوم ﺑﺎﺳﺗﺧﻼص اﻟﻣﻌﻠوﻣﺎت ﻣن اﻟﺑﯾﺎﻧﺎت اﻟﻐﺎﻣﺿﺔ ﻟﺗﻘدﻣﮭﺎ ﺑﻣﺻطﺣﺎت واﺿﺣﺔ وﻣﻔﮭوﻣﺔ ﻟﻠﺟﻣﯾﻊ. اﻟﮭدف اﻷﺳﺎﺳﻲ ﻣن ھذه اﻟﻣراﺟﻌﺔ ھو ﻣﻘﺎرﻧﺔ اﻟﺷﺑﻛﺎت اﻟﻌﺻﺑﯾﺔ ﺑﻐﯾرھﺎ ﻣن أﺳﺎﻟﯾب ﺗﻧﻘﯾب اﻟﺑﯾﺎﻧﺎت وﻟﻌرض ﺑﻌض اﻷﻣﺛﻠﺔ واﻟﺗطﺑﯾﻘﺎت ﻟدوراﻟﺷﺑﻛﺎت اﻟﻌﺻﺑﯾﺔ ﻓﻲ ﺗﻧﻘﯾب اﻟﺑﯾﺎﻧﺎت ﻋﻠﻰ اﻟواﻗﻊ. ABSTRACT: Data Mining accomplishes nontrivial extraction of implicit, previously unknown, and potentially useful information of data. The aim of data mining is to discover knowledge out of data and present it in a form that is easily comprehensible to humans. Neural Networks are analytic techniques capable of predicting new observations from other observations after executing a process of so-called learning from existing data. Neural Network techniques can also be used as a component of analyses designed to build explanatory models. Now there is neural network software that uses sophisticated algorithms directly contributing to the model building process.
    [Show full text]
  • Machine Learning in Water Systems
    Machine Learning in Water Systems Dragan Savic´ (editor) AISB Convention 2013 • University of Exeter • 3rd–5th April, 2013 2 Foreword from the Convention Chairs This volume forms the proceedings of one of eight co-located symposia held at the AISB Convention 2013 that took place 3rd-5th April 2013 at the University of Exeter, UK. The convention consisted of these symposia together in four parallel tracks with five plenary talks; all papers other than the plenaries were given as talks within the symposia. This symposium-based format, which has been the standard for AISB conventions for many years, encourages collabora- tion and discussion among a wide variety of disciplines. Although each symposium is self contained, the convention as a whole represents a diverse array of topics from philosophy, psychology, computer science and cognitive science under the common umbrella of artificial intelligence and the simulation of behaviour. We would like to thank the symposium organisers and their programme committees for their hard work in publicising their symposium, attracting and reviewing submissions and compiling this volume. Without these interesting, high quality symposia the convention would not be possible. Dr Ed Keedwell & Prof. Richard Everson AISB 2013 Convention Chairs Published by The Society for the Study of Artificial Intelligence and the Simulation of Behaviour http://www.aisb.org.uk ISBN: 978-1-908187-33-8 3 CONTENTS Stephen Mounce, A comparative study of artificial neural network architectures for time series prediction of water distribution
    [Show full text]