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    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/13618

    Title: A neuro-fuzzy classifier and its applications
    Authors: Sun, C.-T.
    Jang, J.-S.
    Date: 1993
    Publisher: Institute of Electrical and Electronics Engineers Inc
    Keywords: fuzzy set theory
    artificial intelligence
    neural nets
    pattern recognition
    Abstract: The authors propose a general fuzzy classification scheme with learning ability using an adaptive network. System parameters, such as the membership functions defined for each feature and the parameterized t-norms used to combine conjunctive conditions, are calibrated with backpropagation. To explain this approach, the concept of adaptive networks is introduced and a supervised learning procedure based on a gradient descent algorithm is derived to update the parameters in an adaptive network. The proposed architecture is applied to two problems: two-spiral classification and Iris categorization. From the experimental results, it is concluded that the adaptively adjusted classifier performs well on an Iris classification problem. The results are discussed from the viewpoint of feature selection
    Relation Link: http://webservices.ieee.org/pindex_basic.html
    URI: http://nthur.lib.nthu.edu.tw/handle/987654321/13618
    Appears in Collections:[資訊工程學系] 會議論文
    [資訊系統與應用研究所] 會議論文
    [電腦與通訊科技研發中心] 會議論文

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