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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/57629

    Title: Adaptive Dynamic RBF Fuzzy Neural Controller Design with a Constructive Learning
    Authors: Hsu, Chun-Fei;Lin, Chih-Min;Li, Ming-Chia
    Contributors: 淡江大學電機工程學系
    Keywords: fuzzy system;adaptive control;structuring learning;parameter learning
    Date: 2011-09
    Issue Date: 2011-09-22 20:27:57 (UTC+8)
    Publisher: 台北市:中華民國模糊學會
    Abstract: 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.
    Relation: International Journal of Fuzzy Systems 13(3), pp.175-184
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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