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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/75774

    Title: Evolutionarily adjusting membership functions in Takagi-Sugeno fuzzy systems
    Authors: Hong, Tzung-Pei;Lin, Wei-Tee;Chen, Chun-Hao;Ouyang, Chen-Sen
    Contributors: 淡江大學資訊工程學系
    Date: 2011-05
    Issue Date: 2012-04-13 18:13:27 (UTC+8)
    Publisher: Olney: Inderscience Publishers
    Abstract: Fuzzy set theory has been used more and more frequently in intelligent systems because of its simplicity and similarity to human reasoning. It usually uses a fuzzy inference system to handle new cases for making decisions or controlling actions. In the past, Takagi and Sugeno proposed a well-known fuzzy model, namely TS fuzzy model, to improve the precision of inference results. In this paper, we try to automatically adjust the membership functions appropriate for the TS fuzzy model. A GA-based learning algorithm is thus proposed to achieve the purpose. The proposed approach considers the shapes of membership functions in fitness evaluation in addition to the accuracy. The experimental results show that the proposed approach can derive the membership functions in the Takagi-Sugeno system with low errors and good shapes.
    Relation: International Journal of Intelligent Information and Database Systems 5(3), pp.229–245
    DOI: 10.1504/IJIIDS.2011.040087
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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