Series in Machine Perception and Artificial Intelligence - Vol. 30
COMPENSATORY GENETIC FUZZY NEURAL NETWORKS AND THEIR APPLICATIONS
by Yanqing Zhang (University of South Florida & Georgia Southwestern State University) & Abraham Kandel (University of South Florida & Tel-Aviv University)
This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base, expansion of a sparse fuzzy rule base, fuzzy knowledge discovery, time series prediction, fuzzy games and pattern recognition. This effective soft computing system is able to perform both linguistic-word-level fuzzy reasoning and numerical-data-level information processing. The book also proposes various novel soft computing techniques.
About the Authors
Yanqing Zhang received his BSc and MSc in computer science and engineering from Tianjin University, China, in 1983 and 1986, respectively. He was conferred his PhD in computer science and engineering from the University of South Florida in 1997. From 1986 to 1993, he was a lecturer in the Department of Computer Science and Engineering at Tianjin University. Since 1997, he has been an assistant professor in the School of Computer and Applied Sciences at Georgia Southwestern State University. He is also a member of the IEEE, ACM and Upsilon Pi Epsilon.
Abraham Kandel received his BSc from the Technion-Israel Institute of Technology and MSc from the University of California, both in electrical engineering, and PhD in electrical engineering and computer science from the University of New Mexico. A professor and the Endowed Eminent Scholar in Computer Science and Engineering, he is Chairman of the Department of Computer Science and Engineering at the University of South Florida. He has published over 350 research papers and is a Fellow of the IEEE, ACM, AAAS and the New York Academy of Sciences.
Contents:
- Fuzzy Compensation Principles
- Normal Fuzzy Reasoning
Methodology
- Compensatory Genetic Fuzzy Neural Networks
- Fuzzy Knowledge Rediscovery in Fuzzy Rule Bases
- Fuzzy Cart-Pole Balancing Control Systems
- Fuzzy Knowledge Compression and Expansion
- Highly Nonlinear System Modeling and Prediction
- Fuzzy Moves in Fuzzy Games
- Genetic Neuro-Fuzzy Pattern Recognition
- Constructive Approach to Modeling Fuzzy Systems
Readership: Graduate students, researchers and experts in fuzzy logic,
neural networks and genetic algorithms, and their applications.
| 200pp |
Pub. date: Aug 1998 |
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