Text book of soft computing (CSDT124C) M. Sc.(Sem-II) (Computer Science) According to new CBCS syllabus w. e. f. 2019-20
- 1st ed.
- Pune: Vision publication, 2019
- 358 p. : 21 cm
Contents
1. Introduction to Soft Computing
1. Neural Networks
2. Fuzzy Logic: Definition, Applications
3. Genetic Algorithms: Definition, Applications
2. Neural Network
1. Introduction
2. Fundamental Concepts
3. Artificial Neurons, Neural Networks and Architectures
4. Geometry of Binary Threshold Neurons and their Networks
5. Learning and Memory
6. Linear Separability, Hebb Network, Perceptron Network
7. MSE Error Surface and its Geometry
8. Application of LMS to Noise Cancellation
3. Fuzzy Set Theory
1. Brief Review of Conventional Set Theory
2. Fuzzy Sets
3. Cartesian Product
4. Crisp Relations
5. Fuzzy Relations
6. Tolerance and Equivalence Relations
7. Fuzzy Tolerance and Equivalence Relations
8. Value Assignments
9. Membership Functions
10.Various Forms
11.Fuzzification
12.Defuzzification to Crisp Set
13.a-Cuts for Fuzzy Relations
14.Defuzzification to Scalars
15.Fuzzy Logic
16.Approximate Reasoning
17.Other Forms of Implication Operation
18.Natural Language
19.Linguistic Hedges
20.Fuzzy (Rule Based) System
21.Graphical Techniques of Inference
22.Membership Value Assignments
23.Inference
4. Genetic Algorithms
1. Introduction
2. What is Genetic Algorithm?
3. Why Genetic Algorithm?
4. Robustance of Traditional Optimization and Search Methods
5. Goals of Optimization
6. How are Genetic Algorithms Different from Traditional Methods?
7. Simple GA
8. Genetic Algorithms At Work – A Simulation By Hand
9. Grist for the Search Mill – Important Similarities