Neural Networks, Fuzzy Systems, And Evolutionary Algorithms : Synthesis And Applications

By S. Rajasekaran, G. A. Vijayalakshmi Pai more
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Product Specifications

Publisher PHI Learning All Computer Science books by PHI Learning
ISBN 9788120353343
Author: S. Rajasekaran, G. A. Vijayalakshmi Pai
Number of Pages 574
Available
Available in all digital devices
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Neural Networks, Fuzzy Systems, And Evolutionary Algorithms : Synthesis And Applications - Page 1 Neural Networks, Fuzzy Systems, And Evolutionary Algorithms : Synthesis And Applications - Page 2 Neural Networks, Fuzzy Systems, And Evolutionary Algorithms : Synthesis And Applications - Page 3 Neural Networks, Fuzzy Systems, And Evolutionary Algorithms : Synthesis And Applications - Page 4 Neural Networks, Fuzzy Systems, And Evolutionary Algorithms : Synthesis And Applications - Page 5

Neural Networks, Fuzzy Systems, And Evolutionary Algorithms : Synthesis And Applications by S. Rajasekaran, G. A. Vijayalakshmi Pai
Book Summary:

The second edition of this book provides a comprehensive introduction to a consortium of technologies underlying soft computing, an evolving branch of computational intelligence, which in recent years, has turned synonymous to it. The constituent technologies discussed comprise neural network (NN), fuzzy system (FS), evolutionary algorithm (EA), and a number of hybrid systems, which include classes such as neuro-fuzzy, evolutionary-fuzzy, and neuro-evolutionary systems. The hybridization of the technologies is demonstrated on architectures such as fuzzy backpropagation network (NN-FS hybrid), genetic algorithm-based backpropagation network (NN-EA hybrid), simplified fuzzy ARTMAP (NN-FS hybrid), fuzzy associative memory (NN-FS hybrid), fuzzy logic controlled genetic algorithm (EA-FS hybrid) and evolutionary extreme learning machine (NN-EA hybrid)

Every architecture has been discussed in detail through illustrative examples and applications. The algorithms have been presented in pseudo-code with a step-by-step illustration of the same in problems. The applications, demonstrative of the potential of the architectures, have been chosen from diverse disciplines of science and engineering.

This book, with a wealth of information that is clearly presented and illustrated by many examples and applications, is designed for use as a text for the courses in soft computing at both the senior undergraduate and first-year postgraduate levels of computer science and engineering. It should also be of interest to researchers and technologists desirous of applying soft computing technologies to their respective fields of work

Audience of the Book :
This book Useful for computer Science, M.Sc(Maths), MCA Student.
Table of Contents:

1. Introduction to Artificial Intelligence Systems

Part 1 NEURAL NETWORKS

2. Fundamentals of Neural Networks

3. Backpropagation Networks

4. Associative Memory

5. Adaptive Resonance Theory

6. Extreme Learning Machine 

Part 2 FUZZY SYSTEMS

7. Fuzzy Set Theory

8. Fuzzy Logic and Inreference

9. Type-2 Fuzzy Sets

Part 3 EVOLUTIONARY ALGORITHMS

10. Fundamentals of Genetic Algorithms

11. Genetic Modelling

12. Evolution Strategies 

13. Differential Evolution 

Part 4 HYBRID SYSTEMS

14. Integration of Neural Networks, Fuzzy Logic, and Genetic Algorithms

15. Genetic Algorithm Based Backpropagation Networks

16. Fuzzy Backpropagation Networks

17. Simplified Fuzzy Artmap

18. Fuzzy Associative Memories

19. Fuzzy Logic Controlled Genetic Algorithms

20. Evolutionary Extreme Learning Machine

Index

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