## Abstract

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.

Original language | English |
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Pages (from-to) | 2008-2014 |

Number of pages | 7 |

Journal | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |

Volume | 2 |

Publication status | Published - 1994 Dec 1 |

Event | Proceedings of the 1994 IEEE International Conference on Systems, Man and Cybernetics. Part 1 (of 3) - San Antonio, TX, USA Duration: 1994 Oct 2 → 1994 Oct 5 |

## ASJC Scopus subject areas

- Control and Systems Engineering
- Hardware and Architecture