DocumentCode
3108000
Title
Design and tuning of fuzzy if-then rules for automatic classification
Author
Rotshtein, Alexander ; Katelnikov, Denis
Author_Institution
Dept. of Comput.-Based Inf. & Manage. Syst., Vinnitsa State Tech. Univ., Ukraine
fYear
1998
fDate
20-21 Aug 1998
Firstpage
50
Lastpage
54
Abstract
We propose an approach to the design and tuning of fuzzy rules for automatic classification decision making. This approach is based upon the finding of the weights of fuzzy if-then rules and the shapes of membership functions that minimize the difference between real (desired) and inferred (theoretical) classes of decisions. The problem of fuzzy model tuning is stated as a classical mathematical optimization problem
Keywords
fuzzy logic; inference mechanisms; knowledge acquisition; pattern classification; tuning; uncertainty handling; automatic classification; decision making; fuzzy if-then rules; fuzzy model tuning; mathematical optimization; membership functions; Automatic control; Control systems; Decision making; Fuzzy logic; Fuzzy systems; Information management; Input variables; Medical control systems; Medical diagnostic imaging; Shape;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Information Processing Society - NAFIPS, 1998 Conference of the North American
Conference_Location
Pensacola Beach, FL
Print_ISBN
0-7803-4453-7
Type
conf
DOI
10.1109/NAFIPS.1998.715528
Filename
715528
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