淡江大學機構典藏:Item 987654321/57629
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/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), p.175-184
    DOI: 10.30000/IJFS.201109.0003
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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