NEURAL NETWORKS, FUZZY SYSTEMS AND EVOLUTIONARY ALGORITHMS : SYNTHESIS AND APPLICATIONS, SECOND EDITION
By S. RAJASEKARAN, G.A. VIJAYALAKSHMI PAI
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.
NEW TO THE SECOND EDITION
• New chapters on Extreme Learning Machine, Type-2 Fuzzy Sets, Evolution Strategies, Differential Evolution, and Evolutionary Extreme Learning Machine.
• Revised chapters on Introduction to Artificial Intelligence Systems, Fuzzy Set Theory, and Integration of Neural Networks, Fuzzy Set Theories, and Evolutionary Algorithms.
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1. Introduction to Artificial Intelligence Systems
NEURAL NETWORKS Part 1
2. Fundamentals of Neural Networks
3. Backpropagation Networks
4. Associative Memory
5. Adaptive Resonance Theory
6. Extreme Learning Machine
FUZZY SYSTEMS Part 2
7. Fuzzy Set Theory
8. Fuzzy Logic and Inreference
9. Type-2 Fuzzy Sets
EVOLUTIONARY ALGORITHMS Part 3
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