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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/46355


    Title: Fuzzy system design by a ga-based method for data classification
    Authors: 翁慶昌;Wong, Ching-chang;Lin, Bo-chen;Chen, Chia-chong
    Contributors: 淡江大學電機工程學系
    Date: 2002-04
    Issue Date: 2010-03-26 22:06:34 (UTC+8)
    Publisher: Taylor & Francis
    Abstract: In this paper, a method based on the Genetic Algorithm (GA) and SVD-QR method is proposed to construct an appropriate fuzzy system for data classification. In this method, an individual of the population in the GA is used to determine a fuzzy partition such that some rough fuzzy sets of each input variable are obtained. In order to extract significant fuzzy rules from the rule base of the defined fuzzy system, the SVD-QR method is applied to remove unnecessary fuzzy rules such that the constructed fuzzy system has a low number of fuzzy rules. A fitness function in the GA is considered to guide the search procedure to select an appropriate fuzzy system such that the number of correctly classified patterns are maximized and the number of fuzzy rules is minimized. Finally, a classification problem is considered to illustrate the effectiveness of the proposed method.
    Relation: Cybernetics and Systems 33(3), pp.253-270
    DOI: 10.1080/019697202753551639
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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