Fuzzy B-Spline Membership Function (BMF) and Its Applications in Fuzzy-Neural Control

Chi-Hsu Wang, Pao Shun Tseng

Research output: Contribution to journalArticlepeer-review

133 Scopus citations

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

Original languageEnglish
Pages (from-to)841-851
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics
Volume25
Issue number5
DOIs
StatePublished - 1 Jan 1995

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