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
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;
Conference_Titel :
Fuzzy Information Processing Society - NAFIPS, 1998 Conference of the North American
Conference_Location :
Pensacola Beach, FL
Print_ISBN :
0-7803-4453-7
DOI :
10.1109/NAFIPS.1998.715528
