An algorithmic approach for fuzzy inference

Author

Kim, C.J.

Author_Institution

Dept. of Electr. Eng., Suwon Univ., South Korea

Volume

5

Issue

4

fYear

1997

fDate

11/1/1997 12:00:00 AM

Firstpage

585

Lastpage

598

Abstract

To apply fuzzy logic, two major tasks need to be performed: the derivation of production rules and the determination of membership functions. These tasks are often difficult and time consuming. This paper presents an algorithmic method for generating membership functions and fuzzy production rules; the method includes an entropy minimization for screening analog values. Membership functions are derived by partitioning the variables into the desired number of fuzzy terms and production rules are obtained from minimum entropy clustering decisions. In the rule derivation process, rule weights are also calculated. This algorithmic approach alleviates many problems in the application of fuzzy logic to binary classification

Keywords

entropy; fuzzy logic; inference mechanisms; minimisation; algorithmic approach; analog value screening; binary classification; entropy minimization; fuzzy inference; fuzzy logic; fuzzy production rules; membership functions; minimum entropy clustering decisions; variables partitioning; Automatic control; Clustering algorithms; Data mining; Entropy; Fuzzy control; Fuzzy logic; Inference algorithms; Minimization methods; Partitioning algorithms; Production;

fLanguage

English

Journal_Title

Fuzzy Systems, IEEE Transactions on

Publisher

ieee

ISSN

1063-6706

Type

jour

DOI

10.1109/91.649911

Filename

649911