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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/95869


    Title: Neural Network-Based Fuzzy Systems
    Authors: Su, Mu-Chun;Kao, Chien-Jen;Liu, Kai-Ming
    Contributors: 淡江大學電機工程學系
    Keywords: 模糊類神經網路;模糊規則抽取;函數近似;FDHECNNFuzzy Neural Network;Fuzzy Rule Extraction;Function Approximation;Fdhecnn
    Date: 1994-12
    Issue Date: 2014-02-13 11:13:54 (UTC+8)
    Abstract: In this paper, we discuss how to use FDHECNN's (fuzzy degraded hyperellipsoidal composite neural networks) to extract fuzzy rules for function approximation. The FDHECNN can perform function approximation in the same manner as networks based on Gaussion potential functions, by linear combination of local functions. Furthermore, the output functions of the hidden nodes in the FDHECNN's offer more flexibility than Gaussion potential functions do. A special scheme is developed to find a set of good initial weights in order to speed up the convergence problem. Results of simulations of a system identification demonstrates that the feasibility and robustness of the proposed fuzzy neural networks.
    Relation: 1994 International Computer Symposium Conference Proceeding Volume 2 of 2,頁1246-1250
    Appears in Collections:[電機工程學系暨研究所] 會議論文

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