FUZZY B-SPLINE MEMBERSHIP FUNCTION (BMF) AND ITS APPLICATIONS IN FUZZY-NEURAL CONTROL

A 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 tremendously, This fuzzy-neural control of a model car is well presented in this paper to illustrate the performance and applicability of the proposed method.