Proceedings of the Korean Institute of Intelligent Systems Conference (한국지능시스템학회:학술대회논문집)
- 2003.09b
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- Pages.95-98
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- 2003
Adaptive Fuzzy Neural Control of Unknown Nonlinear Systems Based on Rapid Learning Algorithm
- Kim, Hye-Ryeong (DEPT. OF ELECTRICAL & ELECTRONIC ENG., YONSEI UNIVERSITY) ;
- Kim, Jae-Hun (DEPT. OF ELECTRICAL & ELECTRONIC ENG., YONSEI UNIVERSITY) ;
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Kim, Euntai
(DEPT. OF ELECTRICAL & ELECTRONIC ENG., YONSEI UNIVERSITY) ;
- Park, Mignon (DEPT. OF ELECTRICAL & ELECTRONIC ENG., YONSEI UNIVERSITY)
- Published : 2003.09.01
Abstract
In this paper, an adaptive fuzzy neural control of unknown nonlinear systems based on the rapid learning algorithm is proposed for optimal parameterization. We combine the advantages of fuzzy control and neural network techniques to develop an adaptive fuzzy control system for updating nonlinear parameters of controller. The Fuzzy Neural Network(FNN), which is constructed by an equivalent four-layer connectionist network, is able to learn to control a process by updating the membership functions. The free parameters of the AFN controller are adjusted on-line according to the control law and adaptive law for the purpose of controlling the plant track a given trajectory and it's initial values are off-line preprocessing, In order to improve the convergence of the learning process, we propose a rapid learning algorithm which combines the error back-propagation algorithm with Aitken's
Keywords