Fuzzy B-spline membership function (BMF) and its applications in fuzzy-neural control

Chi Hsu Wang, Wei Yen Wang, Tsu Tian Lee, Pao Shun Tseng

Research output: Contribution to journalConference article

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 languageEnglish
Pages (from-to)2008-2014
Number of pages7
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume2
Publication statusPublished - 1994 Dec 1
EventProceedings of the 1994 IEEE International Conference on Systems, Man and Cybernetics. Part 1 (of 3) - San Antonio, TX, USA
Duration: 1994 Oct 21994 Oct 5

Fingerprint

Membership functions
Splines
Fuzzy neural networks
Railroad cars
Tuning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

Cite this

Fuzzy B-spline membership function (BMF) and its applications in fuzzy-neural control. / Wang, Chi Hsu; Wang, Wei Yen; Lee, Tsu Tian; Tseng, Pao Shun.

In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Vol. 2, 01.12.1994, p. 2008-2014.

Research output: Contribution to journalConference article

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