Evolutionary algorithm based fuzzy modeling using conciseness measure

Author

Suzuki, Takumi ; Furnhashi, T.

Author_Institution

Dept. of Inf. Electron., Nagoya Univ.

Volume

3

fYear

2001

fDate

25-28 July 2001

Firstpage

1575

Abstract

In this paper a fuzzy modeling method using a new conciseness measure is presented. Conciseness of fuzzy models is defined by the shape and allocation of membership functions and the conciseness is quantified by introducing fuzzy entropy. This paper proposes a new measure which evaluates the deviation of a membership function from symmetry. The measure has a different aspect from De Luca and Termini´s (1972) fuzzy entropy measure, which could only evaluate the shape of a membership function. By combining these two measures, a, new measure is derived for evaluation of the shape and allocation of membership functions of a fuzzy model. Numerical results show that the new conciseness measure is effective for fuzzy modeling formulated as a multi-optimization problem

Keywords

evolutionary computation; fuzzy logic; fuzzy set theory; conciseness measure; evolutionary algorithm; fuzzy entropy; fuzzy modeling; membership functions; multioptimization problem; numerical results; Fuzzy sets; Knowledge acquisition; Marine vehicles; Neodymium; Shape;

fLanguage

English

Publisher

ieee

Conference_Titel

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-7078-3

Type

conf

DOI

10.1109/NAFIPS.2001.943784

Filename

943784