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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/39012


    Title: Rule extraction using a novel class of fuzzy degraded hyperellipsoidal composite neural networks
    Authors: Su, Mu-chun;Kao, Chien-jen;Liu, Kai-ming;Liu, Chi-yeh
    Contributors: 淡江大學電機工程學系
    Date: 1995-03-20
    Issue Date: 2010-04-15 11:13:17 (UTC+8)
    Publisher: N.Y.: Institute of Electrical and Electronic Engineers (IEEE)
    Abstract: Presents an innovative approach to rule extraction directly from experimental numerical data for system identification. The authors discuss how to use a novel class of fuzzy degraded hyperellipsoidal composite neural networks (FDHECNN's) to extract fuzzy if-then rules. The fuzzy rules are defined by hyperellipsoids of which principal axes are parallel to the coordinates of the input space. These rules are extracted from the parameters of the trained FDHECNN's. Based on a special learning scheme, the FDHECNN's can evolve automatically to acquire a set of fuzzy rules for approximating the input/output functions considered systems. A highly nonlinear system is used to test the proposed neuro-fuzzy systems.
    Relation: Fuzzy Systems, 1995. International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium., Proceedings of 1995 IEEE Int (Volume:1 ), pp.233-238
    DOI: 10.1109/FUZZY.1995.409686
    Appears in Collections:[電機工程學系暨研究所] 會議論文

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