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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/73595


    Title: Indirect adaptive self-organizing RBF neural controller design with a dynamical training approach
    Authors: Hsu, Chun-Fei;Chiu, Chien-Jung;Tsai, Jang-Zern
    Contributors: 淡江大學電機工程學系
    Keywords: RBF network;Adaptive control;Neural control;Self-organizing;Dynamical learning rate
    Date: 2012-01-01
    Issue Date: 2011-11-29 19:30:24 (UTC+8)
    Publisher: Kidlington: Pergamon
    Abstract: This study proposes an indirect adaptive self-organizing RBF neural control (IASRNC) system which is composed of a feedback controller, a neural identifier and a smooth compensator. The neural identifier which contains a self-organizing RBF (SORBF) network with structure and parameter learning is designed to online estimate a system dynamics using the gradient descent method. The SORBF network can add new hidden neurons and prune insignificant hidden neurons online. The smooth compensator is designed to dispel the effect of minimum approximation error introduced by the neural identifier in the Lyapunov stability theorem. In general, how to determine the learning rate of parameter adaptation laws usually requires some trial-and-error tuning procedures. This paper proposes a dynamical learning rate approach based on a discrete-type Lyapunov function to speed up the convergence of tracking error. Finally, the proposed IASRNC system is applied to control two chaotic systems. Simulation results verify that the proposed IASRNC scheme can achieve a favorable tracking performance.
    Relation: Expert Systems with Applications 39(1), pp.564–573
    DOI: 10.1016/j.eswa.2011.07.047
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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