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.
Author(s): K Sundareswaran
Series: CRC focus
Edition: 2
Publisher: CRC Press
Year: 2019
Language: English
Pages: 126
Tags: Fuzzy Logic; Fuzzy Set; Neural Networks; Genetic Algorithm
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
