Contents

1. Introduction to Fuzzy Logic

1. Illusion

2. Classical Sets

3. Fuzzy Sets

4. Cartesian Product

5. Crisp Relations

6. Fuzzy Relations

7. Tolerance and Equivalence Relations

8. Fuzzy Tolerance and Equivalence Relations

9. Value Assignments

10. Features of Membership Functions

11. Various Forms

12. Fuzzification

13. Defuzzification to Crisp Set

14. l-Cuts for Fuzzy Relations

15. Defuzzification to Scalars

16. Fuzzy Logic

17. Approximate Reasoning

18. Other Forms of Implication Operation

19. Natural Language

20. Linguistic Hedges

21. Fuzzy (Rule Based) System

22. Graphical Techniques of Inference

23. Membership Value Assignments

24. Inference

2. Fuzzy System and Classification

1. Fuzzy System Simulation

2. Fuzzy Classification

3. Zadeh’s Extension Principle

3. Neural Network

1. Introduction

2. Artificial Neural Network

3. Fundamental Concepts

4. Artificial Neurons, Neural Networks and Architectures

5. Geometry of Binary Threshold Neurons and their Networks

6. Learning and Memory

7. Linear Separability, Hebb Network, Perceptron Network

8. MSE Error Surface and its Geometry

9. Application of LMS to Noise Cancellation

4. Genetic Algorithms

1. A Gentle Introduction to Genetic Algorithms

2. Robustance of Traditional Optimization and Search Methods

3. Goals of Optimization

4. How are Genetic Algorithms Different from Traditional Methods?

5. Simple GA

6. Genetic Algorithms At Work – A Simulation By Hand

7. Grist for the Search Mill – Important Similarities

8. Similarity Templates (Schemata)

9. Learning the Lingo