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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/112101


    Title: Design of self-constructing fuzzy wavelet neural control system
    Authors: Tsu-Tian Lee, Po-Chun Wang, Chih-Ching Hsiao, and Chun-Fei Hsu
    Keywords: intelligent control;Lyapunov stability theory;fuzzy neural network;wavelet neural network;self construction
    Date: 2017-06-30
    Issue Date: 2017-11-15 02:10:34 (UTC+8)
    Publisher: IEEE
    Abstract: In this paper, a self-constructing fuzzy wavelet
    neural network (SFWNN) is used to approximate an unknown
    nonlinear term in the system dynamics with the structure and
    parameter learning abilities concurrently. Further, a selfconstructing
    fuzzy wavelet neural control (SFWNC) system,
    which is composed of a computation controller and a robust
    compensator, is proposed. The computation controller using the
    SFWNN approximator is the main controller and the robust
    compensator is designed to eliminate the effect of the
    approximation error. All controller parameters of the SFWNC
    system are adaptively update based on the Lyapunov stability
    theory and the projection algorithm to guarantee the closed-loop
    system stability. Finally, the effectiveness of the proposed
    SFWNC system is verified by simulation results and the SFWNN
    approximator has the admirable property of small fuzzy rules
    size and high learning accuracy.
    Relation: Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems
    DOI: 978-1-5090-4917-2/17/$31.00
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Proceeding

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