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.
|頁（從 - 到）||2008-2014|
|期刊||Proceedings of the IEEE International Conference on Systems, Man and Cybernetics|
|出版狀態||已發佈 - 1994|
|事件||Proceedings of the 1994 IEEE International Conference on Systems, Man and Cybernetics. Part 1 (of 3) - San Antonio, TX, USA|
持續時間: 1994 10月 2 → 1994 10月 5
ASJC Scopus subject areas