國立臺灣師範大學電機工程學系C.-H. WangW.-Y. WangT.-T. LeeP.-S. Tseng2014-10-302014-10-301995-05-010018-9472�http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31975A 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