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dc.contributor.authorC.-H. Wangen_US
dc.contributor.authorW.-Y. Wangen_US
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.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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