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Title: Fuzzy B-spline membership function (BMF) and its applications in fuzzy-neural control
Authors: 國立臺灣師範大學電機工程學系
C.-H. Wang
W.-Y. Wang
T.-T. Lee
P.-S. Tseng
Issue Date: 1-May-1995
Publisher: IEEE Systems, Man, and Cybernetics Society
Abstract: A general methodology for constructing fuzzy membership functions via B-spline curves is proposed. By using the method of least-squares, the authors 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 a B-spline membership function (BMF). By using the local control property of a B-spline curve, the BMFs can be tuned locally during the learning process. For the control of a model car through fuzzy-neural networks, it is shown that the local tuning of BMFs can indeed reduce the number of iterations tremendously. This fuzzy-neural control of a model car is presented to illustrate the performance and applicability of the proposed method
ISSN: 0018-9472�
Other Identifiers: ntnulib_tp_E0604_01_053
Appears in Collections:教師著作

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