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

Author

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

Author_Institution

Sch. of Microelectron. Eng., Griffith Univ., Nathan, Qld., Australia

Volume

25

Issue

5

fYear

1995

fDate

5/1/1995 12:00:00 AM

Firstpage

841

Lastpage

851

Abstract

A general methodology for constructing fuzzy membership functions via B-spline curves is proposed. By using the method of least-squares, the authors 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 a B-spline membership function (BMF). By using the local control property of a B-spline curve, the BMFs can be tuned locally during the learning process. For the control of a model car through fuzzy-neural networks, it is shown that the local tuning of BMFs can indeed reduce the number of iterations tremendously. This fuzzy-neural control of a model car is presented to illustrate the performance and applicability of the proposed method

Keywords

fuzzy control; fuzzy neural nets; fuzzy set theory; neurocontrollers; splines (mathematics); fuzzy B-spline membership function; fuzzy membership functions; fuzzy-neural control; fuzzy-neural networks; learning process; least-squares; local control property; local tuning; model car; Automatic control; Construction industry; Fuzzy control; Fuzzy neural networks; Fuzzy set theory; Process control; Set theory; Spline; System identification; Uncertainty;

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9472

Type

jour

DOI

10.1109/21.376496

Filename

376496