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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/46421


    Title: A clustering-based method for fuzzy modeling
    Authors: 翁慶昌;Wong, Ching-chang;Chen, Chia-chong
    Contributors: 淡江大學電機工程學系
    Date: 1999-06
    Issue Date: 2010-03-26 22:17:41 (UTC+8)
    Publisher: Institute of Electronics, Information and Communication Engineers (IEICE)
    Abstract: In this paper, a clustering-based method is proposed for automatically constructing a multi-input Takagi-Sugeno (TS) fuzzy model where only the input-output data of the identified system are available. The TS fuzzy model is automatically generated by the process of structure identification and parameter identification. In the structure identification step, a clustering method is proposed to provide a systematic procedure to partition the input space so that the number of fuzzy rules and the shapes of fuzzy sets in the premise part are determined from the given input-output data. In the parameter identification step, the recursive least-squares algorithm is applied to choose the parameter values in the consequent part from the given input-output data. Finally, two examples are used to illustrate the effectiveness of the proposed method.
    Relation: IEICE transactions on information and systems E82-D(6), pp.1058-1065
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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