A Learner’s Guide to Fuzzy Logic Systems
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Description
This book presents an introductory coverage of fuzzy logic, including basic principles from an interdisciplinary perspective. It includes concept of evolving a fuzzy set and fuzzy set operations, fuzzification rule base design and defuzzification and simple guidelines for fuzzy sets design and selected applications. Preliminary concepts of Neural Networks and Genetic Algorithm are added features with relevant examples and exercises. It is primarily intended for undergraduate and postgraduate students and researchers to facilitate education in the ever-increasing field of fuzzy logic as medium between human intelligence and machine. Cover Half Title Title Page Copyright Page Dedication List of Figures List of Tables Table of Contents List of Tables Preface to Second Edition Author Chapter 1: Unravelling Uncertainty Through Simple Examples 1.1 Introduction 1.2 Examples 1.3 A Simple View of Fuzzy Logic 1.4 Learning Ability 1.5 Different Phases of Uncertainty 1.5.1 Inexactness 1.5.2 Semantic Ambiguity 1.5.3 Visual Ambiguity 1.5.4 Structural Ambiguity 1.5.5 Undecidability 1.6 Probability and Uncertainty 1.7 Conclusion Questions Chapter 2: Fuzzy Sets 2.1 Introduction 2.2 Classical Sets (Crisp Sets) 2.2.1 A Control Application Using Crisp Set 2.3 Concept of a Fuzzy Set 2.4 Basic Properties and Characteristics of Fuzzy Sets 2.4.1 Universe of Discourse 2.4.2 Fuzzy Set Domain 2.4.3 Shouldered Fuzzy Set 2.4.4 Height of Fuzzy Set 2.4.5 Overlap between Fuzzy Sets 2.4.6 Left and Right Width of Fuzzy Set 2.4.7 Non-Convex and Convex Fuzzy Sets 2.4.8 Commonly Employed Fuzzy Sets 2.5 Crisp Fuzzy Set Operations 2.6 Basic Operations on Fuzzy Sets 2.6.1 Intersection of Fuzzy Sets 2.6.2 Union of Fuzzy Sets 2.6.3 Complement (Negation) of Fuzzy Sets 2.7 Conclusion Questions Chapter 3: Fuzzy Reasoning 3.1 Introduction 3.2 A Conventional Control System 3.3 Major Components of a Fuzzy Logic System 3.3.1 Fuzzification 3.3.2 Inference Engine 3.3.2.1 Rule Base or Fuzzy Propositions 3.3.2.2 Min-Max Method of Implication 3.3.3 Defuzzification Methods 3.3.3.1 Centre of Area Method or Centre of Gravity Method 3.3.3.2 Centre of Sums Method 3.3.3.3 Height Method 3.3.3.4 Centre of Largest Area Method 3.3.3.5 First of Maxima Method 3.3.3.6 Last of Maxima Method 3.3.3.7 Middle of Maxima Method 3.4 Comparison and Evaluation of Deffuzification Methods 3.4.1 Continuity 3.4.2 Unambiguity 3.4.3 Plausibility 3.4.4 Computational Complexity 3.4.5 Weight Counting 3.5 Conclusion Questions Chapter 4: Design Aspects of Fuzzy Systems and Fuzzy Logic Applications 4.1 Introduction 4.2 Suggestions on Fuzzy Set Design 4.3 Extracting Information from Knowledge Engineer 4.4 Adaptive Fuzzy Control 4.5 Fuzzy Decision-Making Solution Solution 4.6 Neuro-Fuzzy Systems 4.7 Fuzzy Genetic Algorithms 4.8 Fuzzy Logic for Genetic Algorithms 4.9 DC Motor Speed Control Using Fuzzy Logic Principle 4.10 Fuzzy Logic–Based Washing Machine 4.11 Conclusion Questions Bibliography
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