DocumentCode :
1532185
Title :
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
Link To Document :
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