Fuzzy rule base optimisation: a pruning and merging approach

Author

Gasso, Komi ; Mourot, Gilles ; Ragot, Jose

Author_Institution

Centre de Recherche en Autom. de Nancy, Vandoeuvre, France

Volume

1

fYear

2000

fDate

2000

Firstpage

67

Abstract

The paper deals with the simplification of the rule base of the Takagi-Sugeno model. The proposed technique assumes an initial lattice partition of the premise space. The complexity of the rule base is optimised through two sequential operations: elimination of the less important rules followed by the merging of neighbouring rules that can describe the same behaviour of the system. The identified structure is further refined by adjusting the parameters of the membership functions. The procedure is illustrated on a simulation example

Keywords

fuzzy logic; fuzzy set theory; knowledge based systems; optimisation; uncertainty handling; Takagi-Sugeno model; fuzzy rule base optimisation; identified structure; initial lattice partition; membership functions; merging approach; neighbouring rules; premise space; pruning; rule base optimisation; sequential operations; simulation example; Evolutionary computation; Fuzzy sets; Genetic algorithms; Lattices; Merging; Nonlinear systems; Optimization methods; System identification; Takagi-Sugeno model; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 2000 IEEE International Conference on

Conference_Location

Nashville, TN

ISSN

1062-922X

Print_ISBN

0-7803-6583-6

Type

conf

DOI

10.1109/ICSMC.2000.884966

Filename

884966