English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 54451/89232 (61%)
造访人次 : 10573399      在线人数 : 32
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/987654321/60752


    题名: A hybrid clustering and gradient descent approach for fuzzy modeling
    作者: 翁慶昌;Wong, Ching-chang;Chen, C.C.
    贡献者: 淡江大學電機工程學系
    关键词: Fuzzy systems;Parameter estimation;Clustering algorithms;Fuzzy sets;Mathematical model;Clustering methods;Nonlinear systems;Inference algorithms;Uncertain systems;System identification
    日期: 1999-12
    上传时间: 2011-10-15 00:49:24 (UTC+8)
    摘要: In this paper, a hybrid clustering and gradient descent approach is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available. The proposed approach is composed of two steps: structure identification and parameter identification. In the process of structure identification, a clustering method is proposed to provide a systematic procedure to determine the number of fuzzy rules and construct an initial fuzzy model from the given input-output data. In the process of parameter identification, the gradient descent method is used to tune the parameters of the constructed fuzzy model to obtain a more precise fuzzy model from the given input-output data. Finally, two examples of nonlinear system are given to illustrate the effectiveness of the proposed approach.
    關聯: IEEE transactions on systems, man and cybernetics 29(6), pp.686-693
    DOI: 10.1109/3477.809024
    显示于类别:[電機工程學系暨研究所] 期刊論文

    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