淡江大學機構典藏:Item 987654321/95870
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 58615/92280 (64%)
造访人次 : 560532      在线人数 : 59
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/95870


    题名: A Neuro-Fuzzy Approach to System Identification
    作者: Su, Mu-Chun;Kao, Chien-Jen
    贡献者: 淡江大學電機工程學系
    关键词: 系統識別;類神經網路;模糊理論;System Identification;Neural Network;Fuzzy Theory
    日期: 1994-12
    上传时间: 2014-02-13 11:14:02 (UTC+8)
    摘要: In this paper, we present an innovative approach to the identification of non-linear systems. The proposed neuro-fuzzy system identifier employs a hybrid clustering and least mean squared error (LMS) algorithm. The neuro- fuzzy system under consideration is implemented as an two- layer FHRCNN (fuzzy hyperrectangular composite neural network). The SDDL (supervised decision-directed learning) algorithm is used to find a set of hyperrectangles defined by the parameters of hidden nodes while the LMS algorithm estimates the connection weights from hidden nodes to output nodes. Furthermore, based on the hybrid learning rule, the fuzzy neural networks can evolve automatically to acquire a set of fuzzy if-then rules for approximating the input/output functions of considered systems. A highly nonlinear system is used to test the proposed neural-fuzzy systems. The simulation results demonstrate its feasibility and robustness.
    關聯: Proceedings of 1994 International Symposium on Artificial Neural Networks,頁495-500
    显示于类别:[電機工程學系暨研究所] 會議論文

    文件中的档案:

    没有与此文件相关的档案.

    在機構典藏中所有的数据项都受到原著作权保护.

    TAIR相关文章

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