DOCSLIB.ORG

PDF Full Paper

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
PDF Full Paper Techniques for Developing Large Scale Fuzzy Logic Systems Jon C. Ervin Sema E. Alptekin Apogee Research Group California Polytechnic State University Los Osos, CA 93402, USA San Luis Obispo, CA 93407, USA [email protected] [email protected] property of the formulation can quickly Abstract overwhelm available computing resources in even relatively modest sized applications. One In this paper, we describe various promising extension, Intuitionistic Fuzzy Logic techniques used to make Intuitionistic (IFL), can further exacerbate memory problems Fuzzy Logic Systems amenable to by effectively doubling the number of input operating on applications with large membership functions. Atanassov [1] introduced numbers of inputs. A rule reduction the notion of pairing non-membership functions technique known as Combs method is in IFL with the usual membership functions of combined with an automated tuning Fuzzy Logic. process based on Particle Swarm Optimization. A second stage of tuning A number of researchers have developed on rule weights results in improved techniques to mitigate the problem “exponential performance and further reduction in rule explosion” or “curse of dimensionality” the size of the rule-base. The entire through various means [8, 10, and 14], however, process has been developed to operate these methods generally tend to be complex and within the Matlab software limited to special cases. One promising environment. The technique is tested exception is a technique developed by William against the Wisconsin Breast Cancer Database. The use of these tools shows Combs that changes the problem from one of an great promise in significantly exponential dependence to a linear dependence expanding the range and complexity of on the number of inputs [4]. The use of Combs problems that can be addressed using method also simplifies rule-base generation and Atanassov’s Intuitionistic Fuzzy Logic. makes it easier to automate tuning of Fuzzy systems. Keywords: Atanassov’s Intuitionistic Fuzzy Another issue that must be addressed when Logic, Particle Swarm Optimization, Combs implementing a Fuzzy Logic algorithm for a Method. specific application is constructing membership 1 Introduction functions for each input that provide adequate system performance. A common approach taken The decision making paradigm known as Fuzzy is to interview experts in the particular field and Logic has been implemented in a wide range of use their feedback to build the fuzzy applications since it was first introduced by Dr. membership functions. This process can be Lotfi Zadeh in his seminal paper published in cumbersome and/or completely impractical for 1965 [17]. A number of researchers have since large problems, leading to the need to made major contributions in expanding and implement some sort of automated membership adapting Fuzzy Logic beyond its initial function construction and optimization process. conception. One major Achilles heel for Fuzzy Logic applications has been that in most current A number of methods for automating this algorithms, memory requirements expand process have been published in the literature. A exponentially with linear growth in inputs. This common approach has been to somehow L. Magdalena, M. Ojeda-Aciego, J.L. Verdegay (eds): Proceedings of IPMU'08, pp. 806{811 Torremolinos (M´alaga),June 22{27, 2008 construct membership functions and then use a found in the literature discussing the validity of population based optimization method such as the claims made for the URC and the interested the Genetic Algorithm to tune them. We chose reader is referred to the references [3, 5, 12, and to follow a variation of this approach by 15] for detail on this topic. Our own experience constructing the membership functions and to date has been that the URC performs as well using a method known as Particle Swarm or better than the IRC formulation. Optimization (PSO) for tuning. The PSO algorithm, first developed by Eberhart and We used Combs method to apply Atanassov’s Kennedy [6], was inspired by the coordinated Intuitionistic Fuzzy Logic to the well known group behaviors of animals such as flocks of Wisconsin Breast Cancer Database (WBCD) birds in flight. One important advantage of this [16]. An initial set of four IFL membership and technique is the simple formulation and non-membership functions were developed for implementation of the underlying equations. each of the nine input variables of the WBCD. The 683 unique samples in this database The main focus of our research has been to associate various diagnostic tests as real-valued combine several techniques to simplify the inputs with a binary output of benign/malignant tuning process and make it practical to address for a suspect tissue mass. The WBCD was problems of much greater size and complexity chosen as a test case because it provides a [7]. Recently, we have built on previous relatively large number of inputs, allowing us to research to further improve the optimization demonstrate the rule-base reduction advantage process. We believe our efforts have brought us of the URC method. The entire rule-base closer to realizing an efficient, automated consisted of 36 rules (9 inputs x 4 process to generate Fuzzy Logic systems membership/non-membership functions) versus capable of handling large and complex the 262,144 (49) maximum number of rules that problems. could be used in the IRC method. The following section provides a brief Figure 1 shows the distribution of the values of background of Combs method and the database one of nine WBCD input variables with respect used to verify our approach. The next section to benign/malignancy of the tissue mass. On this describes the PSO optimization process. chart a diagnosis of benign is marked as an “x” Following that we provide some of our results and a diagnosis of malignant is marked with an and end with a section on our conclusions. “o”. The distribution shows a strong correspondence between low values and a 2 Combs URC Method diagnosis of benign, however there are a number of exceptions to this generalization. This William Combs refers to his method as the distribution is characteristic to a greater or lesser union rule configuration (URC) versus what he degree in all nine of the input variables. calls the intersection rule configuration (IRC) of the traditional fuzzy rule-base construct [4]. The main difference between the URC and IRC is 10 o o o o o o oo oo oo ooo ooooo xoo oo oooooo o oooo o oooo ooo o xx o ooo ooooo oooooooooo ooo o oo oooo o that every rule in the rule-base of the URC is oo o ooooooooo oooooo oo o o ooooo o o ooooo o 9 oo o o oo oo o o o o required to have only one antecedent for every o o o o 8 o x o consequent. Initially, this may sound counter- ooo o o o o o x o o oo o o 7 o o o intuitive as a means of reducing the number of o x o oo o o rules, however by imposing this restriction it 6 o o o means that each membership function of all the Value o o 5 o x o o o o o o x x o x input variables is used only once in the o o x x o o o 4 o x oo o x o x x o o o o x antecedent of a rule in the rule base. Each of o o x o o Relative x x o o o o o x x o 3 ox o x o o x o these rules are joined by a logical OR in the o x x o x x o o x xo 2 x o x ox x x x o xo rule-base, hence the designation union rule x xxxx xx x o o xo x o x x x xxo configuration. ooo x x x 1 xxxxxxx xx xx xxx xx xx xx xxxx xxxox xxxxxxxxox xxxx xxxxxxxxxxx xxxoxxxxxxxxoxxxx xxxxo xxxxxxox xx xxx xxx xxxxxx xxxxxo xxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxx xoxxxxxxxxxx xxxxooxoxx x xxxxxxxxxxxx xxx xxxxx xxxxxx xxxxx xx xx xxxxxxxxxxxxx xxxxxxxxx 0 xoxxx x xx xxxxxoxoxx xxx xx xxxxxxxxxx xxxxxxxxx xxxxxxxxxx xxxoxxxxxoxxxxxxxx Combs and his various co-authors show that the 0 100 200 300 400 500 600 700 entire problem space can be accessed by Data Point implementing the URC. A spirited debate can be Figure 1: Distribution of Input “Bare Nuclei” Proceedings of IPMU'08 807 3 Fuzzy Classification Model function described a predominately low or high input value. This action left two points of each In the spirit of simplifying the development and membership function to float freely over the tuning processes, the entire optimization input range and it was these two values that algorithm was created, debugged and executed were subjected to the optimization process. within the Matlab software suite [11]. The intuitionistic fuzzy classifier was created in the The total number of optimization variables was: Matlab Fuzzy Logic Toolbox. A set of (9 input variables) x (2 points per function) x (4 customized Matlab m-files provide overall functions per input variable) = 72 variables. control of the optimization process. The m-files make calls to the PSO and IFL modules, with Figure 2 shows an example of the the program terminating after a user-defined (non)membership function for one input variable number of iterations have been completed. with the 8 points subjected to optimization circled. Here we abbreviate “membership and The Fuzzy Logic Toolbox provides a number of non membership” with “(non)membership”. general default values for the user to choose from in building the fuzzy classification model. For instance, the software allows the user to Not Low choose between two methods, minimum or product, to fill in for the “AND” function in the Not High rule antecedent.
Load more

Recommended publications
  • Does Informal Logic Have Anything to Learn from Fuzzy Logic?
    University of Windsor Scholarship at UWindsor OSSA Conference Archive OSSA 3 May 15th, 9:00 AM - May 17th, 5:00 PM Does informal logic have anything to learn from fuzzy logic? John Woods University of British Columbia Follow this and additional works at: https://scholar.uwindsor.ca/ossaarchive Part of the Philosophy Commons Woods, John, "Does informal logic have anything to learn from fuzzy logic?" (1999). OSSA Conference Archive. 62. https://scholar.uwindsor.ca/ossaarchive/OSSA3/papersandcommentaries/62 This Paper is brought to you for free and open access by the Conferences and Conference Proceedings at Scholarship at UWindsor. It has been accepted for inclusion in OSSA Conference Archive by an authorized conference organizer of Scholarship at UWindsor. For more information, please contact [email protected]. Title: Does informal logic have anything to learn from fuzzy logic? Author: John Woods Response to this paper by: Rolf George (c)2000 John Woods 1. Motivation Since antiquity, philosophers have been challenged by the apparent vagueness of everyday thought and experience. How, these philosophers have wanted to know, are the things we say and think compatible with logical laws such as Excluded Middle?1 A kindred attraction has been the question of how truth presents itself — relatively and in degrees? in approximations? in resemblances, or in bits and pieces? F.H. Bradley is a celebrated (or as the case may be, excoriated) champion of the degrees view of truth. There are, one may say, two main views of error, the absolute and relative. According to the former view there are perfect truths, and on the other side there are sheer errors..
    [Show full text]
  • Decision Making: Fuzzy Logic
    First, a bit of history, my 1965 paper on fuzzy sets was motivated by my feeling that the then existing theories provided no means of dealing with a pervasive aspect of reality—unsharpness (fuzziness) of class boundaries. Without such means, realistic models of human-centered and biological systems are hard to construct. My expectation was that fuzzy set theory would be welcomed by the scientific communities in these and related fields. Contrary to my expectation, in these fields, fuzzy set theory was met with skepticism and, in some instances, with hostility. What I did not anticipate was that, for many years after the debut of fuzzy set theory, its Decision Making: main applications would be in the realms of engineering systems and consumer products. Fuzzy Logic … Underlying the concept of a linguistic variable is a fact which is widely unrecognized—a fact which relates to the 2018-03-15 concept of precision. Precision has two distinct meanings—precision in value and precision in meaning. The first meaning is traditional. The second meaning is not. The second meaning is rooted in fuzzy logic. -- Lotfi Zadeh, 2013 Questions (N-2) 1. What are the 2 most “complex” decision making techniques we’ve seen? 2. What are their strengths? Weaknesses? 3. What is the key (insight) to their success? 4. What does Planning have that (forward chaining) RBS do not? 5. When do we need a communication mechanism? 2 Questions (N-1) 1. Cooperative problem solving / distributed expertise is using h___ to d__ problems into smaller parts. 2. R__ experts rarely communicate/collaborate.
    [Show full text]
  • Fuzzy Logic Control for Stand-Alone Photovoltaic Energy Conversion
    Fuzzy Logic Control for Stand-alone Photovoltaic Energy Conversion System, and Innovation in Renewable Energy By Zainab Almukhtar A Thesis Submitted to Saint Mary’s University, Halifax, Nova Scotia in Partial Fulfillment of the Requirements for the Degree of Master of Technology Entrepreneurship and Innovation June 29, 2016, Halifax, Nova Scotia Copyright Zainab Almukhtar, 2016 Approved: Dr. Adel Merabet Supervisor Division of Engineering Approved: Dr. Claudia De Fuentes Supervisory Committee Member Management Department, Sobey School of Business Approved: Dr. Rasheed Beguenane External Examiner ECE, Royal Military College Date: June 29th, 2016 Fuzzy Logic Control for Stand-alone Photovoltaic Energy Conversion System, and Innovation in Renewable Energy. by Zainab Almukhtar Abstract In this dissertation, simulation and hardware emulation was implemented to experiment the operation of a power regulation system for stand-alone PV system with DC loads using Fuzzy Logic Control (FLC). The system encompasses the functions of Maximum Power Point Tracking (MPPT) to bring the power to the maximum value, load power regulation and control of battery operation. An algorithm that tracks the maximum power, and the corresponding fuzzy logic controller were developed. An improved method for the battery operation regulation including a fuzzy logic controller was applied. Load voltage regulation was achieved by a modified cascaded PI controller. The power regulation system managed to stabilize the load power, proved fast MPPT tracking and regulation of the battery operation in the presence of fluctuations and fast input variations. The work included a study of the importance of university-industry collaboration to innovation in renewable energy. The current collaboration channels and the contribution of the current work was analysed through a questionnaire directed to the supervisor.
    [Show full text]
  • The Use of Fuzzy Logic for Artificial Intelligence in Games
    The use of Fuzzy Logic for Artificial Intelligence in Games Michele Pirovano1;2 1Department of Computer Science, University of Milano, Milano, Italy 2Dipartimento di Ingegneria Elettronica e Informazione, Politecnico di Milano, Italy December 7, 2012 Abstract Game AI, like all other aspects of a videogame, exists to pro- pel the higher goal of a videogame: to be fun and entertain. In Game artificial intelligence (game AI) is the branch of other words, the motto of the game AI society is often thought videogame development that is concerned with empowering to be If the player cannot see it, why do it? [21]. The context games with the illusion of intelligence. Game AI borrows is however not so simple nowadays, as partly due to successful many techniques from the broader field of AI, from simple fi- products that have brought forward advanced AI, partly due to nite state machines to state-of-the-art evolutionary algorithms. the ubiquitous online multi-player options that have changed Among these techniques, fuzzy logic is one of the tools that the habits of players, players are now more demanding in re- must be present in the arsenal of a good videogame AI de- gards to AI and more and more game developers have turned veloper, due to the simplicity of its formulation coupled with their attention from scripted and predictable agents to more its expressive power. In this paper, we introduce the field of human-like agents, capable of learning and thus, in the clas- game AI and review the use of fuzzy logic in games, looking sical AI definition, intelligent.
    [Show full text]
  • Applications of Fuzzy Sets Theory
    Francesco Masulli Sushmita Mitra Gabriella Pasi (Eds.) Applications of Fuzzy Sets Theory 7th International Workshop on Fuzzy Logic and Applications, WILF 2007 Camogli, Italy, July 7-10, 2007 Proceedings 4u Springer Table of Contents Advances in Fuzzy Set Theory From Fuzzy Beliefs to Goals 1 Cilia da Costa Pereira and Andrea G.B. Tettamanzi Information Entropy and Co-entropy of Crisp and Fuzzy Granulations 9 Daniela Bianucci, Gianpiero Cattaneo, and Davide Ciucd Possibilistic Linear Programming in Blending and Transportation Planning Problem 20 Bilge Bilgen Measuring the Interpretive Cost in Fuzzy Logic Computations 28 Pascual Julian, Gines Moreno, and Jaime Penabad A Fixed-Point Theorem for Multi-valued Functions with an Application to Multilattice-Based Logic Programming 37 Jesus Medina, Manuel Ojeda-Aciego, and Jorge Ruiz-Calvino Contextualized Possibilistic Networks with Temporal Framework for Knowledge Base Reliability Improvement 45 Marco Grasso, Michele Lavagna, and Guido Sangiovanni Reconstruction of the Matrix of Causal Dependencies for the Fuzzy Inductive Reasoning Method . 53 Guido Sangiovanni and Michele Lavagna Derivative Information from Fuzzy Models 61 Paulo Salgado and Fernando Gouveia The Genetic Development of Uninorm-Based Neurons 69 Angelo Ciaramella, Witold Pedrycz, and Robertfi Tagliaferri Using Visualization Tools to Guide Consensus in Group Decision Making 77 Sergio Alonso, Enrique Herrera- Viedma, Francisco Javier Cabrerizo, Carlos Porcel, and A.G. Lopez-Herrera Reconstruction Methods for Incomplete Fuzzy Preference
    [Show full text]
  • A Thesis Entitled Kidney Compatibility Score Generation for a Donor – Recipient Pair Using Fuzzy Logic by Sampath Kumar Yella
    A Thesis entitled Kidney Compatibility Score Generation for a Donor – Recipient pair Using Fuzzy Logic by Sampath Kumar Yellanki Submitted to Graduate Faculty as partial fulfillment of the requirements for The Master of Science Degree in Engineering ___ _______________________________________ Dr. Devinder Kaur, Committee Chair __________________________________________ Dr. Michael Rees, M.D., Committee Member __________________________________________ Dr. Ezzatollah Salari, Committee Member __________________________________________ Dr. Patricia R. Komuniecki, Dean College of Graduate Studies The University of Toledo December 2012 Copyright 2012, Sampath Kumar Yellanki This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of Kidney Compatibility Score Generation for a Donor – Recipient pair Using Fuzzy Logic by Sampath Kumar Yellanki Submitted to Graduate School as a partial fulfillment of the requirements for The Master of Science Degree in Engineering The University of Toledo December 2012 This thesis, proposes and implements a Fuzzy Logic based Hierarchical model to address the problem of filtering the donor recipient pairs by predicting a kidney compatibility score. Donor – Recipient pair incompatibility is one of the major problems encountered in Renal Transplantation. Kidney Paired Donation is a barter system where the pairs exchange the donor organs to overcome the disadvantage of incompatibility. Identification of such pairs requires a Kidney Transplant Surgeon to evaluate the compatibility of swapped pairs. Unfortunately, such incompatible pairs run into huge numbers which is a herculean task for a surgeon and can prone to human fatigue. This work presents a Hierarchical System developed based on Fuzzy Logic to determine the quality of a Kidney Transplant based on various input parameters.
    [Show full text]
  • Techniques for Developing Large Scale Fuzzy Logic Systems
    Techniques for Developing Large Scale Fuzzy Logic Systems Jon C. Ervin Sema E. Alptekin Apogee Research Group California Polytechnic State University Los Osos, CA 93402, USA San Luis Obispo, CA 93407, USA [email protected] [email protected] property of the formulation can quickly Abstract overwhelm available computing resources in even relatively modest sized applications. One In this paper, we describe various promising extension, Intuitionistic Fuzzy Logic techniques used to make Intuitionistic (IFL), can further exacerbate memory problems Fuzzy Logic Systems amenable to by effectively doubling the number of input operating on applications with large membership functions. Atanassov [1] introduced numbers of inputs. A rule reduction the notion of pairing non-membership functions technique known as Combs method is in IFL with the usual membership functions of combined with an automated tuning Fuzzy Logic. process based on Particle Swarm Optimization. A second stage of tuning A number of researchers have developed on rule weights results in improved techniques to mitigate the problem “exponential performance and further reduction in rule explosion” or “curse of dimensionality” the size of the rule-base. The entire through various means [8, 10, and 14], however, process has been developed to operate these methods generally tend to be complex and within the Matlab software limited to special cases. One promising environment. The technique is tested exception is a technique developed by William against the Wisconsin Breast Cancer Database. The use of these tools shows Combs that changes the problem from one of an great promise in significantly exponential dependence to a linear dependence expanding the range and complexity of on the number of inputs [4].
    [Show full text]
  • Fuzzy Systems, IEEE Transactions On
    368 IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 7, NO. 3, JUNE 1999 Correspondence Will the Real Iris Data Please Stand Up? TABLE I THE IRIS DATA:FISHER [2] James C. Bezdek, James M. Keller, Raghu Krishnapuram, Ludmila I. Kuncheva, and Nikhil R. Pal Abstract—This correspondence points out several published errors in replicates of the well-known Iris data, which was collected in 1935 by Anderson [1], but first published in 1936 by Fisher [2]. Index Terms—Iris data. I. INTRODUCTION AND CONCLUSIONS While preparing Kuncheva and Bezdek [3], these authors discov- ered that there are (at least) two distinct published replicates of the Iris data that have been used as a basis for an unknown number of papers. Subsequently, Bezdek et al. [4] discovered two different errors in the version of Iris available through the well-known University of California at Irvine (UCI) machine learning database. Reproduced below, from the preface of Bezdek et al. [4] is an account of the problems errors like this cause. The data: Most of the numerical examples (in [4]) use small data sets that may seem contrived to you—and some of them are. There is much to be said for the pedagogical value of using a few points in the plane when studying and illustrating properties of various models. On the other hand, there are certain risks too. Sometimes conclusions that are legitimate for small specialized data sets become invalid in the face of large numbers of samples, features and classes. And, of course, time and space complexity make their presence felt in very unpredictable ways as problem size grows.
    [Show full text]
  • What Does Urban Transformation Look Like? Findings from a Global Prize Competition
    sustainability Communication What Does Urban Transformation Look Like? Findings from a Global Prize Competition Anne Maassen * and Madeleine Galvin World Resources Institute, 10 G Street NE, Washington, DC 20002, USA * Correspondence: [email protected] Received: 14 June 2019; Accepted: 21 August 2019; Published: 27 August 2019 Abstract: Different disciplines are grappling with the concept of ‘urban transformation’ reflecting its planetary importance and urgency. A recent systematic review traces the emergence of a normative epistemic community that is concerned with helping make sustainable urban transformation a reality. Our contribution to this growing body of work springs out of a recent initiative at the World Resources Institute, namely, the WRI Ross Prize for Cities, a global award for transformative projects that have ignited sustainable changes in their city. In this paper we explain the competition-based approach that was used to source transformative initiatives and relate our findings to existing currents in urban transformation scholarship and key debates. We focus on one of the questions at the heart of the normative urban transformation agenda: what does urban transformation look like in practice? Based on an analysis of the five finalists, we describe urban transformation as encompassing a plurality of contextual and relative changes, which may progress and accelerate positively, or regress over time. An evaluative approach that considers varying ‘degrees’ and ‘types’ of urban transformation is proposed to establish meaning within single cases and across several cases of urban transformation. Keywords: urban; transformation; urbanization; research; interdisciplinary; transitions; systemic change 1. Introduction There has been a lively debate among urban scholars and practitioners about the potential and mechanisms of ‘urban transformation’.
    [Show full text]
  • Resume on “Reply of William E. Combs About the Article of Jerry M
    Resume on “Reply of William E. Combs about the article of Analogously we can divide the consequent in three single Jerry M. Mendel and Qilian Liang” by Wilmer Garzón A input/output fuzzy rules: IF Humidity is Low THEN the Fan Speed is Low. IF Humidity is Average THEN the Fan Speed is Index Terms – Combinatorial problem, Combs method, curse of dimensionality, intersection rule configuration (IRC) and union rule Moderate. configuration (URC). IF Humidity is High THEN the Fan Speed is High. As stated in [3], the Mamdani fuzzy reasoning method is I. INTRODUCTION comprised of two inference steps. This paper has how objective reply four points mentioned in the • “The IF … THEN fuzzy implication is defined as a paper or Mendel and Liang about Fuzzy Rule Configuration. conjunction (AND) of the antecedent and consequent; it is usually implemented as the MIN (∧) aggregating operator though other t-norms are possible.” • “The logical connective between the rules, ALSO, is II. DISCUSSION assumed to be a disjunction (OR). It is usually implemented as the MAX ( ) aggregating operator. We can also use other Reply to Point #1: Generalized Modus Ponens and . t-conorms.” With one example can be represented a system with two input and one output, they are talking about fan speed system, the inputs are Grouping the last six expressions as obtained: temperature and humidity and the output is fan speed; one intersection rule configuration is: IF temperature is cool THEN the fan speed is low OR IF Temperature is Hot and humidity is Low THEN IF humidity is low
    [Show full text]
  • M.Tech (Full Time) – KNOWLEDGE ENGINEERING Curriculum & Syllabus (2008-2009)
    M.Tech (Full Time) – KNOWLEDGE ENGINEERING Curriculum & Syllabus (2008-2009) Faculty of Engineering & Technology SRM University SRM Nagar, Kattankulathur – 603 203 S.R.M. UNIVERSITY SCHOOL OF COMPUTER SCIENCE & ENGINEERING M.Tech (Knowledge Engineering Curriculum & Syllabus (2008-2009) I SEMESTER Subject Code Subject Name L T P C Theory MA0533 Mathematical Foundations of Computer Science 3 0 0 3 CS0541 Artificial Intelligence & Intelligent Systems 3 0 3 4 CS0543 Knowledge Based System Design 3 2 0 4 CS0545 Data & Knowledge Mining 3 2 0 4 Elective – I 3 0 0 3 Total 15 4 3 18 II SEMESTER Subject Code Subject Name L T P C Theory CS0540 Semantic Web 3 2 0 4 CS0542 Knowledge Based Neural Computing 3 0 3 4 CS0544 Agent Based Learning 3 2 0 4 Elective – II 3 0 0 3 Elective – III* 3 0 0 3 Total 15 4 3 18 * Elective – III shall be an Inter Departmental (or) Inter School Elective III SEMESTER Subject Code Subject Name L T P C Theory 3 0 0 3 Elective –IV 3 0 0 3 Elective - V 3 0 0 3 Elective - VI 3 0 0 3 CS0550 Seminar 0 2 0 1 Practical CS0645 Project Phase - I 0 0 12 6 Total 9 2 12 16 IV SEMESTER Subject Code Subject Name L T P C CS0646 Project Phase - II 0 0 36 18 Total 0 0 36 18 TOTAL CREDITS TO BE EARNED : 70 1 Electives for First Semester Subject Code Subject Name L T P C CS0685 Multimedia Systems 3 0 0 3 CS0561 Geographical Information Systems 3 0 0 3 CS0563 Professional Studies 3 0 0 3 CS0553 Genetic Algorithms & Machine Learning 3 0 0 3 Electives for Second Semester Subject Code Subject Name L T P C CS0650 Pattern Recognition Techniques
    [Show full text]
  • Decision Making: Fuzzy Logic
    Decision Making: Fuzzy Logic 2016-06-28 Questions (N-3) 1. How can we describe decision making? 2. What do the algorithms we’ve seen share? 3. What are the dimensions we tend to assess? 4. FSMs/Btrees: ____ :: Planning : _____ 5. For the 2nd blank, we need m_____s. 6. When is reactive appropriate? Deliberative? 7. What is the ‘hot-potato’ passed around (KE)? 8. H______ have helped in most approaches. 9. Which approach should you use? Questions (N-2) 1. What are the 2 most “complex” decision making techniques we’ve seen? 2. What are their strengths? Weaknesses? 3. What is the key (insight) to their success? 4. What is typically necessary to support this insight (hint: used in Planning + RBS)? 5. What does Planning have that (forward chaining) RBS do not? 6. When do we need a communication mechanism? Questions (N-1) 1. Cooperative problem solving / distributed expertise is using h___ to d__ problems into smaller parts. 2. R__ experts rarely communicate/collaborate. 3. Three types of communication are… 4. The three main parts of a Blackboard are… 5. An Arbiter can be used to… enables a computer to reason about linguistic terms and rules in a way similar to humans FUZZY LOGIC 1. Cut two slices of bread medium thick. 2. Turn the heat on the griddle on high. 3. Grill the slices on one side until golden brown. 4. Turn the slices over and add a generous helping of cheese. 5. Replace and grill until the top of the cheese is slightly brown. 6. Remove, sprinkle on a small amount of black pepper, and eat.
    [Show full text]