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Dynamic System Identification Using a Recurrent Compensatory Fuzzy Neural Network  

Lee, Chi-Yung (Dept. of Computer Science and Information Engineering, Nankai Institute of Technology)
Lin, Cheng-Jian (Dept. of Computer Science and Engineering, National Chin-Yi University of Technology)
Chen, Cheng-Hung (Dept. of Electrical and Control Engineering, National Chiao-Tung University)
Chang, Chun-Lung (Mechanical and Systems Research Laboratories, Industrial Technology Research Institute)
Publication Information
International Journal of Control, Automation, and Systems / v.6, no.5, 2008 , pp. 755-766 More about this Journal
Abstract
This study presents a recurrent compensatory fuzzy neural network (RCFNN) for dynamic system identification. The proposed RCFNN uses a compensatory fuzzy reasoning method, and has feedback connections added to the rule layer of the RCFNN. The compensatory fuzzy reasoning method can make the fuzzy logic system more effective, and the additional feedback connections can solve temporal problems as well. Moreover, an online learning algorithm is demonstrated to automatically construct the RCFNN. The RCFNN initially contains no rules. The rules are created and adapted as online learning proceeds via simultaneous structure and parameter learning. Structure learning is based on the measure of degree and parameter learning is based on the gradient descent algorithm. The simulation results from identifying dynamic systems demonstrate that the convergence speed of the proposed method exceeds that of conventional methods. Moreover, the number of adjustable parameters of the proposed method is less than the other recurrent methods.
Keywords
Chaotic; compensatory operator; fuzzy neural networks; identification; recurrent networks;
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