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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/46109

    Title: Adaptive design of a fuzzy cerebellar model arithmetic controller neural network
    Authors: Chen, J. Y.;Tsai, P. S.;翁慶昌;Wong, Ching-chang
    Contributors: 淡江大學電機工程學系
    Date: 2005-03-01
    Issue Date: 2010-03-26 20:57:58 (UTC+8)
    Publisher: Institution of Engineering and Technology (IET)
    Abstract: Adaptation fuzzy cerebellar model arithmetic controller (CMAC) neural networks are considered. Adaptation mechanisms for a fuzzy CMAC neural network are proposed to enable the construction of indirect and direct control laws. These control laws are then used to enhance the robustness of a closed-loop control system. It is shown that the fuzzy CMACs can cope with the system's uncertainties using adaptation with no preliminary off-line learning phase being required. The adaptation laws are derived using a Lyapunov stability analysis, so that both system tracking stability and error convergence can be guaranteed in the closed-loop system. Simulation results from the two systems show a satisfactory performance of the proposed control schemes even in the presence of modelling uncertainties.
    Relation: IEE Proceedings-Control Theory & Applications 152(2), pp.133-137
    DOI: 10.1049/ip-cta:20041117
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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