標題: Adaptive asymmetric fuzzy neural network controller design via network structuring adaptation
作者: Hsu, Chun-Fei
Lin, Ping-Zong
Lee, Tsu-Tian
Wang, Chi-Hsu
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: fuzzy neural network;asymmetric Gaussian membership function;structure adaptation algorithm;adaptive control
公開日期: 16-Oct-2008
摘要: This paper proposes a self-structuring fuzzy neural network (SFNN) using asymmetric Gaussian membership functions in the structure and parameter learning phases. An adaptive self-structuring asymmetric fuzzy neural-network control (ASAFNC) system which consists of an SFNN controller and a robust controller is proposed. The SFNN controller uses an SFNN with structure and parameter learning phases to online mimic an ideal controller, simultaneously. The structure learning phase consists of the growing-and-pruning algorithms of fuzzy rules to achieve an optimal network structure, and the parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. The robust controller is designed to compensate for the modeling error between the SFNN controller and the ideal controller. An online training methodology is developed in the Lyapunov sense, and thus the stability of the closed-loop control system can be guaranteed. Finally, the proposed ASAFNC system is applied to a second-order chaotic dynamics system. The simulation results show that the proposed ASAFNC can achieve favorable tracking performance. (C) 2008 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.fss.2008.01.034
http://hdl.handle.net/11536/8242
ISSN: 0165-0114
DOI: 10.1016/j.fss.2008.01.034
期刊: FUZZY SETS AND SYSTEMS
Volume: 159
Issue: 20
起始頁: 2627
結束頁: 2649
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