Principles of Soft Computing /
S. N. Sivanandam and S. N. Deepa
- 3rd Ed.
- New Delhi Wiley India Pvt. Ltd. 2019
- xxvii, 760p. : ill. ; 28cm
It includes bibliography, sample question papers and Index Pages
This book is meant for a wide range of readers, who wish to learn the basic concepts of soft computing. It can also be useful for programmers, researchers and management experts who use soft computing techniques. The basic concepts of soft computing are dealt in detail with the relevant information and knowledge available for understanding the computing process. The various neural network concepts are explained with examples, highlighting the difference between various architectures. Fuzzy logic techniques have been clearly dealt with suitable examples. Genetic algorithm operators and the various classifications have been discussed in lucid manner, so that a starter can understand the concepts with a minimal effort.
Table of Contents: Chapter 1 Introduction 1.1 Neural Networks 1.2 Application Scope of Neural Networks 1.3 Fuzzy Logic 1.4 Genetic Algorithm 1.5 Hybrid Systems 1.6 Soft Computing 1.7 Summary Chapter 2 Artificial Neural Network: An Introduction 2.1 Fundamental Concept 2.2 Evolution of Neural Networks 2.3 Basic Models of Artificial Neural Network 2.4 Important Terminologies of ANNs 2.5 McCulloch–Pitts Neuron 2.6 Linear Separability 2.7 Hebb Network 2.8 Summary 2.9 Solved Problems 2.10 Review Questions 2.11 Exercise Problems 2.12 Projects Chapter 3 Supervised Learning Network 3.1 Introduction 3.2 Perceptron Networks 3.3 Adaptive Linear Neuron (Adaline) 3.4 Multiple Adaptive Linear Neurons 3.5 Back-Propagation Network 3.6 Radial Basis Function Network 3.7 Time Delay Neural Network 3.8 Functional Link Networks 3.9 Tree Neural Networks 3.10 Wavelet Neural Networks 3.11 Summary 3.12 Solved Problems 3.13 Review Questions 3.14 Exercise Problems 3.15 Projects Chapter 4 Associative Memory Networks 4.1 Introduction 4.2 Training Algorithms for Pattern Association 4.3 Autoassociative Memory Network 4.4 Heteroassociative Memory Network 4.5 Bidirectional Associative Memory (BAM) 4.6 Hopfield Networks 4.7 Iterative Autoassociative Memory Networks 4.8 Temporal Associative Memory Network 4.9 Summary 4.10 Solved Problems 4.11 Review Questions 4.12 Exercise Problems 4.13 Projects Chapter 5 Unsupervised Learning Networks 5.1 Introduction 5.2 Fixed Weight Competitive Nets 5.3 Kohonen Self-Organizing Feature Maps 5.4 Learning Vector Quantization 5.5 Counterpropagation Networks 5.6 Adaptive Resonance Theory Network 5.7 Summary 5.8 Solved Problems 5.9 Review Questions 5.10 Exercise Problems 5.11 Projects Chapter 6 Special Networks 6.1 Introduction 6.2 Simulated Annealing Network 6.3 Boltzmann Machine 6.4 Gaussian Machine 6.5 Cauchy Machine 6.6 Probabilistic Neural Net 6.7 Cascade Correlation Network 6.8 Cognitron Network 6.9 Neocognitron Network 6.10 Cellular Neural Network 6.11 Logicon Projection Network Model 6.12 Spatio-Temporal Connectionist Neural Network 6.13 Optical Neural Networks 6.14 Neuroprocessor Chips 6.15 Ensemble Neural Network Models 6.16 Summary 6.17 Review Questions Chapter 7 Third-Generation Neural Networks 7.1 Introduction 7.2 Spiking Neural Networks 7.3 Convolutional Neural Networks 7.4 Deep Learning Neural Networks 7.5 Extreme Learning Machine Model 7.6 Summary 7.7 Review Questions Chapter 8 Clustering of Self-Organizing Feature Maps 8.1 Introduction 8.2 Concept of Clustering 8.3 Training of SOMs 8.4 Clustering of SOM: Method I 8.5 Clustering of SOM: Method II 8.5 Summary 8.6 Review Questions Chapter 9 Stability Analysis of a Class of Artificial Neural Network Systems 9.1 Introduction 9.2 Stability Conditions of a Class of Non-Linear Systems 9.3 Formation of Main Matrices and Sub-Matrices for an Artificial Neural Network System 9.4 Methodology Developed for Stability Analysis of Artificial Neural Networks 9.5 Summary 9.6 Solved Problems 9.7 Review Questions 9.8 Exercise Problems Chapter 10 Introduction to Fuzzy Logic, Classical Sets and Fuzzy Sets 10.1 Introduction to Fuzzy Logic 10.2 Classical Sets (Crisp Sets) 10.3 Fuzzy Sets 10.4 Summary 10.5 Solved Problems 10.6 Review Questions 10.7 Exercise Problems Chapter 11 Classical Relations and Fuzzy Relations 11.1 Introduction 11.2 Cartesian Product of Relation 11.3 Classical Relation 11.4 Fuzzy Relations 11.5 Tolerance and Equivalence Relations 11.6 Noninteractive Fuzzy Sets 11.7 Summary 11.8 Solved Problems 11.9 Review Questions 11.10 Exercise Problems Chapter 12 Membership Function 12.1 Introduction 12.2 Features of the Membership Functions 12.3 Fuzzification 12.4 Methods of Membership Value Assignments 12.5 Summary 12.6 Solved Problems 12.7 Review Questions 12.8 Exercise Problems Chapter 13 Defuzzification 13.1 Introduction 13.2 Lambda-Cuts for Fuzzy Sets (Alpha-Cuts) 13.3 Lambda-Cuts for Fuzzy Relations 13.4 Defuzzification Methods 13.5 Summary 13.6 Solved Problems 13.7 Review Questions 13.8 Exercise Problems Chapter 14 Fuzzy Arithmetic and Fuzzy Measures 14.1 Introduction 14.2 Fuzzy Arithmetic 14.3 Extension Principle 14.4 Fuzzy Measures 14.5 Measures of Fuzziness 14.6 Fuzzy Integrals 14.7 Summary 14.8 Solved Problems 14.9 Review Questions 14.10 Exercise Problems Chapter 15 Fuzzy Rule Base and Approximate Reasoning 15.1 Introduction 15.2 Truth Values and Tables in Fuzzy Logic 15.3 Fuzzy Propositions 15.4 Formation of Rules 15.5 Decomposition of Rules (Compound Rules) 15.6 Aggregation of Fuzzy Rules 15.7 Fuzzy Reasoning (Approximate Reasoning) 15.8 Fuzzy Inference Systems (FIS) 15.9 Overview of Fuzzy Expert System 15.10 Summary 15.11 Review Questions 15.12 Exercise Problems Chapter 16 Fuzzy Decision Making 16.1 Introduction 16.2 Individual Decision Making 16.3 Multiperson Decision Making 16.4 Multiobjective Decision Making 16.5 Multiattribute Decision Making 16.6 Fuzzy Bayesian Decision Making 16.7 Summary 16.8 Review Questions 16.9 Exercise Problems Chapter 17 Fuzzy Logic Control Systems 17.1 Introduction 17.2 Control System Design 17.3 Architecture and Operation of FLC System 17.4 FLC System Models 17.5 Application of FLC Systems 17.6 Summary 17.7 Review Questions 17.8 Exercise Problems Chapter 18 Fuzzy Cognitive Maps 18.1 Cognitive Maps – Base for FCM 18.2 Fundamentals of FCM 18.3 Dynamics of FCM and Its Activation Function 18.4 Applications of FCM 18.5 Summary 18.6 Review Questions Chapter 19 Type-2 Fuzzy Sets and Embedded Fuzzy Sets 19.1 Basic Concepts and Definition of Type-2 Fuzzy Sets 19.2 Set Theoretic and Algebraic Operations on Type-2 Fuzzy Sets 19.3 Properties of Membership Grades 19.4 Cartesian Product of Type-2 Fuzzy Sets 19.5 Composition of Type-2 Fuzzy Sets 19.6 Interval Type-2 Fuzzy Sets 19.7 Applications of Type-2 Fuzzy Sets 19.8 Embedded Fuzzy Sets 19.9 Summary 19.10 Review Questions Chapter 20 Stability Analysis of Certain Classes of Fuzzy Systems 20.1 Stability Analysis of Fuzzy Systems given by System Matrices 20.2 Numerical Illustrations for Fuzzy System Stability 20.3 Stability Analysis of Fuzzy Systems represented by Relational Matrices 20.4 Stabilization and Stability Analysis of an Inverted Pendulum Motion using Fuzzy Logic Controller 20.5 Summary 20.6 Review Questions 20.7 Exercise Problems Chapter 21 Genetic Algorithm 21.1 Introduction 21.2 Biological Background 21.3 Traditional Optimization and Search Techniques 21.4 Genetic Algorithm and Search Space 21.5 Genetic Algorithm vs. Traditional Algorithms 21.6 Basic Terminologies in Genetic Algorithm 21.7 Simple GA 21.8 General Genetic Algorithm 21.9 Operators in Genetic Algorithm 21.10 Stopping Condition for Genetic Algorithm Flow 21.11 Constraints in Genetic Algorithm 21.12 Problem Solving Using Genetic Algorithm 21.13 The Schema Theorem 21.14 Classification of Genetic Algorithm 21.15 Holland Classifier Systems 21.16 Genetic Programming 21.17 Advantages and Limitations of Genetic Algorithm 21.18 Applications of Genetic Algorithm 21.19 Summary 21.20 Review Questions 21.21 Exercise Problems Chapter 22 Differential Evolution Algorithm 22.1 Differential Evolution – Process Flow and Operators 22.2 Selection of DE Control Parameters 22.3 Schemes of Differential Evolution 22.4 Numerical Illustration of DE Algorithm for a Simple Function Optimization 22.5 Applications of Differential Evolution 22.6 Summary 22.7 Review Questions Chapter 23 Hybrid Soft Computing Techniques 23.1 Introduction 23.2 Neuro-Fuzzy Hybrid Systems 23.3 Genetic Neuro-Hybrid Systems 23.4 Genetic Fuzzy Hybrid and Fuzzy Genetic Hybrid Systems 23.5 Simplified Fuzzy ARTMAP 23.6 Summary 23.7 Solved Problems using MATLAB 23.8 Review Questions 23.9 Exercise Problems xxiv Chapter 24 Applications of Soft Computing 24.1 Introduction 24.2 A Fusion Approach of Multispectral Images with SAR (Synthetic Aperture Radar) Image for Flood Area 24.3 Optimization of Traveling Salesman Problem using Genetic Algorithm Approach 24.4 Genetic Algorithm-Based Internet Search Technique 24.5 Soft Computing Based Hybrid Fuzzy Controllers 24.6 Soft Computing Based Rocket Engine Control 24.7 Summary 24.8 Review Questions 24.9 Exercise Problems Chapter 25 Soft Computing Techniques Using C and C++ 25.1 Introduction 25.2 Neural Network Implementation 25.3 Fuzzy Logic Implementation 25.4 Genetic Algorithm Implementation 25.5 Summary 25.6 Exercise Problems Chapter 26 MATLAB Environment for Soft Computing Technique 26.1 Introduction 26.2 Getting Started with MATLAB 26.3 Introduction to Simulink 26.4 MATLAB Neural Network Toolbox 26.5 Fuzzy Logic MATLAB Toolbox 26.6 Genetic Algorithm MATLAB Toolbox 26.7 Neural Network MATLAB Source Codes 26.8 Fuzzy Logic MATLAB Source Codes 26.9 Genetic Algorithm MATLAB Source Codes 26.10 Summary 26.11 Exercise Problems
Bibliography Sample Question Paper 1 Sample Question Paper 2 Sample Question Paper 3 Sample Question Paper 4 Sample Question Paper 5 Index xx