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.
|Number of pages||11|
|Journal||IEEE Transactions on Systems, Man, and Cybernetics|
|State||Published - 1 Jan 1995|