Principles of Soft Computing, 3ed

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
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