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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/38699

    Title: Generating fuzzy rules by a GA-based method from input-output data
    Other Titles: 從輸出入資料中建立模糊規則之以遺傳演算為基的方法
    Authors: Wong, Ching-chang;Chen, Chia-chong
    Contributors: 淡江大學電機工程學系
    Date: 1999-10
    Issue Date: 2010-04-15 11:37:55 (UTC+8)
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Abstract: A method based on the concepts of the genetic algorithm (GA) and recursive least-squares method is proposed to construct a fuzzy system directly from some gathered input-output data of the discussed problem. The proposed method can find an appropriate fuzzy system with fewer rules to approach an identified system under the condition that the constructed fuzzy system must satisfy a predetermined acceptable performance. In this method, each individual in the GA is constructed to determine the number of fuzzy rules and the premise part of the fuzzy system, and the recursive least-squares method is used to determine the consequent part of the constructed fuzzy system described by this individual. Finally, two identification problems of nonlinear systems are utilized to illustrate the efficiency of the proposed method.
    Relation: Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on (Volume:5 ), pp.278-283
    DOI: 10.1109/ICSMC.1999.815561
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Proceeding

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