Neural Networks, Fuzzy Systems and Evolutionary Algorithms
Synthesis and Applications
2nd Edition
Published on 30. July 2017
Book
Paperback/Softback
572 pages
978-81-203-5334-3 (ISBN)
Description
The second edition of the 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.
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.
More details
Edition
2nd Revised edition
Language
English
Place of publication
New Delhi
India
Target group
College/higher education
Edition type
Revised edition
Dimensions
Height: 235 mm
Width: 178 mm
Thickness: 30 mm
Weight
747 gr
ISBN-13
978-81-203-5334-3 (9788120353343)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
S. Rajasekaran, D.Sc. (Civil Engineering) FNAE, is Visiting Professor, Department of Civil Engineering, PSG College of Technology, Coimbatore. He has 50 years of teaching and research experience and published more than 300 research papers in international and national journals. He was Alexander von Humboldt Fellow and recipient of several awards such as AICTE Award for Outstanding Academic and AICTE Award for Outstanding Researcher. His areas of special interest include Structural Engineering, Finite Element Analysis and Application of Soft Computing to Structural Engineering, Structural Dynamics and Stability of Structures.
G.A. Vijayalakshmi PAI, Ph.D., SMIEEE, is an Associate Professor, Department of Computer Applications, PSG College of Technology, Coimbatore. Her research interests span the fields of Computational Intelligence, Computational Finance, Machine Learning and Pattern Recognition. She has investigated many research projects in the field of Computational Intelligence and its applications, funded by government agencies. She was the recipient of the AICTE Career Award for Young Teachers, 2001-a National Award given to young talented teachers who have established competence in their area of specialization, by the All India Council of Technical Education, New Delhi, India.
G.A. Vijayalakshmi PAI, Ph.D., SMIEEE, is an Associate Professor, Department of Computer Applications, PSG College of Technology, Coimbatore. Her research interests span the fields of Computational Intelligence, Computational Finance, Machine Learning and Pattern Recognition. She has investigated many research projects in the field of Computational Intelligence and its applications, funded by government agencies. She was the recipient of the AICTE Career Award for Young Teachers, 2001-a National Award given to young talented teachers who have established competence in their area of specialization, by the All India Council of Technical Education, New Delhi, India.
Content
- Preface
- 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 LOGIC
- 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
- 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 LOGIC
- 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