English  |  正體中文  |  简体中文  |  Items with full text/Total items : 52047/87178 (60%)
Visitors : 8706498      Online Users : 240
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/93537

    Title: Intelligent control of chaotic systems via self-organizing Hermite-polynomial-based neural network
    Authors: Hsu, Chun-Fei
    Contributors: 淡江大學電機工程學系
    Keywords: Adaptive control;Neural control;Hermite polynomial function;Self-organizing;Lyapunov stability;Chaotic system
    Date: 2014-01
    Issue Date: 2014-01-22 15:29:05 (UTC+8)
    Publisher: Amsterdam:Elsevier BV
    Abstract: This paper proposes an adaptive self-organizing Hermite-polynomial-based neural control (ASHNC) system which is composed of a neural controller and a supervisor compensator. The neural controller uses a self-organizing Hermite-polynomial-based neural network (SHNN) to approximate an ideal feedback controller. For the SHNN, the developed self-organizing approach is clearly and easily used for real-time systems and the parameter learning ability is effective with high convergence precision and fast convergence time. The supervisor compensator is designed to eliminate the approximation error between the neural controller and ideal feedback controller without chattering phenomena. Moreover, a proportional–integral (PI) type adaptation law is derived based on the Lyapunov stability theory; thus not only the system stability of the control system can be guaranteed but also the convergence of the tracking error can be speeded up. Finally, the proposed ASHNC system is applied to a chaotic system. Simulation results demonstrate that the proposed ASHNC system can achieve favorable control performance.
    Relation: Neurocomputing 123, pp.197–206
    DOI: 10.1016/j.neucom.2013.07.008
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback