English  |  正體中文  |  简体中文  |  Items with full text/Total items : 55025/89277 (62%)
Visitors : 10606447      Online Users : 30
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/987654321/106361

    Title: Intelligent dynamic sliding-mode neural control using recurrent perturbation fuzzy neural networks
    Authors: Hsu, Chun-Fei;Chang, Chun-Wei
    Keywords: fuzzy neural network;recurrent neural network;intelligent control;sine-cosine perturbed function
    Date: 2016-01-15
    Issue Date: 2016-04-22 13:47:25 (UTC+8)
    Publisher: Elsevier BV
    Abstract: In this paper, a recurrent perturbation fuzzy neural network (RPFNN) is used to online approximate an unknown nonlinear term in the system dynamics. A sine-cosine perturbed membership function is used to handle rule uncertainties when it is hard to exactly determine the grade of the value of fuzzy sets. Unlike type-2 fuzzy sets use an extra type reduction operation to find the output, the proposed RPFNN does not require heavy computational loading. Meanwhile, this paper proposes an intelligent dynamic sliding-mode neural control (IDSNC) system which is composed of a neural controller and an exponential compensator.
    Relation: Neurocomputing 173(pt.3), pp.734-743
    DOI: 10.1016/j.neucom.2015.08.024
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

    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