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    政大機構典藏 > 商學院 > 企業管理學系 > 期刊論文 >  Item 140.119/64245
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/64245

    Title: Continuous genetic algorithm-based fuzzy neural network for learning fuzzy IF-THEN rules
    Authors: Kuo,R.J.;Hong, S.M.;Lin,Y.;Huang, Y.C.
    Contributors: 企管系
    Keywords: Fuzzy neural networks;Continuous genetic algorithms
    Date: 2008-08
    Issue Date: 2014-02-26 15:38:49 (UTC+8)
    Abstract: This study proposes a fuzzy neural network (FNN) that can process both fuzzy inputs and outputs. The continuous genetic algorithm (CGA) is employed to enhance its performance. Both the simulation and real-world problem results show that the proposed CGA-based FNN can obtain the relationship between fuzzy inputs and outputs. CGA can not only shorten the training time but also increase the accuracy for the FNN.
    Relation: Neurocomputing, 71(13-15), 2893-2907
    Source URI: http://dx.doi.org/10.1016/j.neucom.2007.07.013
    Data Type: article
    DOI 連結: http://dx.doi.org/http://dx.doi.org/10.1016/j.neucom.2007.07.013
    DOI: 10.1016/j.neucom.2007.07.013
    Appears in Collections:[企業管理學系] 期刊論文

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