Please use this identifier to cite or link to this item: http://rportal.lib.ntnu.edu.tw:80/handle/20.500.12235/32069
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dc.contributor國立臺灣師範大學電機工程學系zh_tw
dc.contributor.authorC.-H. Wangen_US
dc.contributor.authorW.-Y. Wangen_US
dc.date.accessioned2014-10-30T09:28:26Z-
dc.date.available2014-10-30T09:28:26Z-
dc.date.issued1994-10-05zh_TW
dc.identifierntnulib_tp_E0604_02_092zh_TW
dc.identifier.urihttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/32069-
dc.description.abstractA general methodology for constructing fuzzy membership functions via B-spline curve is proposed. By using the method of least-squares, we translate the empirical data into the form of the control points of B-spline curves to construct fuzzy membership functions. This unified form of fuzzy membership functions is called as B-spline membership functions (BMF's). By using the local control property of B-spline curve, the BMF's can be tuned locally during learning process. For the control of a model car through fuzzy-neural networks, it is shown that the local tuning of BMF's can indeed reduce the number of iterations tremendouslyen_US
dc.description.urihttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=400147zh_TW
dc.languageenzh_TW
dc.relationIEEE International Conference on Systems, Man and Cybernetics, vol. 2, San Antonio, TX,pp. 2008-2014en_US
dc.titleFuzzy B-spline membership function (BMF) and its applications in fuzzy-neural controlen_US
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