Adaptive Dynamic RBF Fuzzy Neural Controller Design with a Constructive Learning

doi:10.30000/IJFS.201109.0003

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 期刊論文 > Item 987654321/57629

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题名:	Adaptive Dynamic RBF Fuzzy Neural Controller Design with a Constructive Learning
作者:	Hsu, Chun-Fei;Lin, Chih-Min;Li, Ming-Chia
贡献者:	淡江大學電機工程學系
关键词:	fuzzy system;adaptive control;structuring learning;parameter learning.
日期:	2011-09
上传时间:	2011-09-22 20:27:57 (UTC+8)
出版者:	台北市：中華民國模糊學會
摘要:	Radial basis function (RBF) network can be viewed as a fuzzy rule base with specified membership functions and fuzzy inference operations. However, there is a trade-off between the approximation performance of RBF network and the number of hidden neurons. To tackle this problem, this paper proposes a dynamic RBF (DRBF) network with a constructive learning. This DRBF network not only can create the new hidden neurons, but also can prune the insignificant hidden neurons. Then, an adaptive dynamic RBF fuzzy neural control (ADRFNC) system, including a neural controller and a saturation compensator, is developed. The neural controller uses a DRBF network to on-line mimic an ideal controller and the saturation compensator is designed to dispel the approximation error introduced by the neural controller. Finally, the proposed ADRFNC system is applied to a chaotic circuit and a DC motor control system. Simulation and experimental results show the proposed ADRFNC system can achieve a favorable tracking performance when the controller’s parameters have been learned and the network structure has been constructed by the proposed learning algorithm
關聯:	International Journal of Fuzzy Systems 13(3), p.175-184
DOI:	10.30000/IJFS.201109.0003
显示于类别:	[電機工程學系暨研究所] 期刊論文

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