This is the first unified treatment of fuzzy modeling and fuzzy control, providing tools for control of complex nonlinear systems. Coverage includes model complexity, precision, and computing time. The book is useful for electrical, computer, chemical, mechanical and aeronautical engineers.

Fuzzy logic methodology has been proven effective in dealing with complex nonlinear systems containing uncertainties that are otherwise difficult to model. Technology based on this methodology has been applied to many real-world problems, especially in the area of consumer products. This book presents the first unified and thorough treatment of fuzzy modeling and fuzzy control, providing necessary tools for the control of complex nonlinear systems. Careful consideration is given to questions concerning model complexity, model precision, and computing time.

In addition to being an excellent reference for electrical, computer, chemical, industrial, civil, manufacturing, mechanical and aeronautical engineers, the book may also be appropriate for classroom use in a graduate course in electrical engineering, computer engineering, and computer science. Applied mathematicians, control engineers, computer scientists, and physicists will benefit from the presentation as well.

First thorough and unified treatment of fuzzy modeling and fuzzy control that provides necessary tools for the control of complex nonlinear systems Technology based on fuzzy logic methodology has many pratical applications, especially in the area of consumer products Excellent reference volume for control, electrical, computer, chemical, industrial, civil, manufacturing and aeronautical engineers; computer scientists; applied mathematicians; and physical scientists Textbook for a graduate course in electrial engineering, computer engineering, or computer science

Autorentext

Hongjing Liang received his B.S. degree in mathematics from Bohai University, Jinzhou, China, in 2009, his M.S. degree in fundamental mathematics from Northeastern University, Shenyang, China, in 2011, and a Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2016. He is currently working at Bohai University. His research interests include multi-agent systems, complex systems, and output regulation. Huaguang Zhang received his B.S. and M.S. degrees in control engineering from Northeastern Electric Power University, Jilin, China, 1982 and 1985, respectively, and a Ph.D. degree in thermal power engineering and automation from Southeast University, Nanjing, China, in 1991. Dr. Zhang joined the Department of Automatic Control, Northeastern University, Shenyang, China, in 1992, as a Postdoctoral Fellow. Since 1994, he has been a professor and the head of the Electric Automation Institute, Northeastern University. He has authored three English monographs and holds 30 patents. His main research interests are neural network-based control, fuzzy control, chaos control, nonlinear control, signal processing, adaptive dynamic programming (ADP) and their industrial applications. Dr. Zhang was a recipient of the Nationwide Excellent Post-Doctor, the Outstanding Youth Science Foundation Award from the National Natural Science Foundation Committee of China in 2003, the Cheung Kong Scholar Award from the Education Ministry of China in 2005, and the IEEE Transactions on Neural Networks Outstanding Paper Award in 2012. He was an Associate Editor of Automatica, IEEE Transactions on Cybernetics and Neurocomputing. He is the Deputy Director of the Intelligent System Engineering Committee of Chinese Association of Artificial Intelligence.

Inhalt
Fuzzy Set Theory and Rough Set Theory.- Identification of the Takagi-Sugeno Fuzzy Model.- Fuzzy Model Identification Based on Rough Set Data Analysis.- Identification of the Fuzzy Hyperbolic Model.- Basic Methods of Fuzzy Inference and Control.- Fuzzy Inference and Control Methods Involving Two Kinds of Uncertainties.- Fuzzy Control Schemes via a Fuzzy Performance Evaluator.- Multivariable Predictive Control Based on the T-S Fuzzy Model.- Adaptive Control Methods Based on Fuzzy Basis Function Vectors.- Controller Design Based on the Fuzzy Hyperbolic Model.- Fuzzy H ? Filter Design for Nonlinear Discrete-Time Systems with Multiple Time-Delays.- Chaotification of the Fuzzy Hyperbolic Model.- Feedforward Fuzzy Control Approach Using the Fourier Integral.