TY  - BOOK
AU  - Sardesai,A.
TI  - Text book of soft computing (CSDT124C): M. Sc.(Sem-II) (Computer Science) According to new CBCS syllabus w. e. f. 2019-20
U1  - 005.15 23rd ed.
PY  - 2019///
CY  - Pune
PB  - Vision publication
KW  - Soft computing
KW  - FYMSc-Sem -II
KW  - Computer science
N1  - 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

10.Similarity Templates (Schemata)  

11.Learning the Lingo
ER  -