Title :
Tuning method of linguistic membership functions
Author :
Chung, Byeong-Mook ; Oh, Ju-Ho
Author_Institution :
Production Eng. Res. Lab., LUCKY-GOLDSTAR Co. Ltd., Kyunggi, South Korea
Abstract :
A learning method which can tune the linguistic membership functions is presented. To accomplish such a method, the fuzzy inference mechanism must be expressed by the membership functions without losing a physical sense. The fuzzy subsets are then described by only a few membership functions. When both the control input and the linguistic membership function are tuned in the fuzzy controller, the fuzzy rules after the training can be expressed by the linguistic membership functions maintaining their physical meaning
Keywords :
computational linguistics; fuzzy control; fuzzy set theory; inference mechanisms; learning (artificial intelligence); fuzzy controller; fuzzy inference mechanism; fuzzy rules; fuzzy subsets; learning method; linguistic membership functions; Fuzzy control; Fuzzy sets; Inference mechanisms; Input variables; Learning systems; Niobium; Precision engineering; Production engineering; Quantization;
Conference_Titel :
Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1896-X
DOI :
10.1109/FUZZY.1994.343720
