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## Book Description

Soft computing is a branch of computer science that deals with a family of methods that imitate human intelligence. This is done with the goal of creating tools that will contain some human-like capabilities (such as learning, reasoning and decision-making). This book covers the entire gamut of soft computing, including fuzzy logic, rough sets, artificial neural networks, and various evolutionary algorithms. It offers a learner-centric approach where each new concept is introduced with carefully designed examples/instances to train the learner.

1. Cover
2. Title page
3. Contents
5. Dedication
6. Preface
7. Chapter 1: Introduction
8. Chapter 2: Fuzzy Sets
1. 2.1 Crisp Sets: A Review
2. 2.2 Fuzzy Sets
3. 2.3 Fuzzy Membership Functions
4. 2.4 Operations on Fuzzy Sets
5. 2.5 Fuzzy Relations
6. 2.6 Fuzzy Extension Principle
7. Chapter Summary
8. Solved Problems
11. Exercises
12. Bibliography and Historical Notes
9. Chapter 3: Fuzzy Logic
1. 3.1 Crisp Logic: A Review
2. 3.2 Fuzzy Logic Basics
3. 3.3 Fuzzy Truth in Terms of Fuzzy Sets
4. 3.4 Fuzzy Rules
5. 3.5 Fuzzy Reasoning
6. Chapter Summary
7. Solved Problems
10. Exercises
11. Bibliography and Historical Notes
10. Chapter 4: Fuzzy Inference Systems
1. 4.1 Introduction
2. 4.2 Fuzzification of the Input Variables
3. 4.3 Application of Fuzzy Operators on the Antecedent Parts of the Rules
4. 4.4 Evaluation of the Fuzzy Rules
5. 4.5 Aggregation of Output Fuzzy Sets Across the Rules
6. 4.6 Defuzzification of the Resultant Aggregate Fuzzy Set
7. 4.7 Fuzzy Controllers
8. Chapter Summary
9. Solved Problems
12. Exercises
13. Bibliography and Historical Notes
11. Chapter 5: Rough Sets
12. Chapter 6: Artificial Neural Networks: Basic Concepts
1. 6.1 Introduction
2. 6.2 Computation in Terms of Patterns
3. 6.3 The McCulloch–Pitts Neural Model
4. 6.4 The Perceptron
5. 6.5 Neural Network Architectures
6. 6.6 Activation Functions
7. 6.7 Learning by Neural Nets
8. Chapter Summary
9. Solved Problems
12. Exercises
13. Bibliography and Historical Notes
13. Chapter 7: Pattern Classifiers
14. Chapter 8: Pattern Associators
1. 8.1 Auto-associative Nets
2. 8.2 Hetero-associative Nets
3. 8.3 Hopfield Networks
4. 8.4 Bidirectional Associative Memory
5. Chapter Summary
6. Solved Problems
9. Exercises
10. Bibliography and Historical Notes
15. Chapter 9: Competitive Neural Nets
1. 9.1 The MAXNET
2. 9.2 Kohonen’s Self-organizing Map (SOM)
3. 9.3 Learning Vector Quantization (LVQ)
4. 9.4 Adaptive Resonance Theory (ART)
5. Chapter Summary
6. Solved Problems
9. Exercises
10. Bibliography and Historical Notes
16. Chapter 10: Backpropagation
1. 10.1 Multi-layer Feedforward Net
2. 10.2 The Generalized Delta Rule
3. 10.3 The Backpropagation Algorithm
4. Chapter Summary
5. Solved Problems
8. Exercises
9. Bibliography and Historical Notes
17. Chapter 11: Elementary Search Techniques
1. 11.1 State Spaces
2. 11.2 State Space Search
3. 11.3 Exhaustive Search
4. 11.4 Heuristic Search
5. 11.5 Production Systems
6. Chapter Summary
7. Solved Problems
10. Exercises
11. Bibliography and Historical Notes
18. Chapter 12: Advanced Search Strategies
1. 12.1 Natural Evolution: A Brief Review
2. 12.2 Genetic Algorithms (GAs)
3. 12.3 Multi-objective Genetic Algorithms
4. 12.4 Simulated Annealing
5. Chapter Summary
6. Solved Problems