DocumentCode
2359453
Title
Generating fuzzy rules by genetic algorithms
Author
Mohammadian, M. ; Stonier, R.J.
Author_Institution
Dept. of Comput. Sci., Edith Cowan Univ., Perth, WA, Australia
fYear
1994
fDate
18-20 Jul 1994
Firstpage
362
Lastpage
367
Abstract
A general method is developed to generate fuzzy rules by using genetic algorithms (GAs) and a fuzzy logic controller (FLC). By using GAs as a learning procedure and a FLC as the system´s performance evaluator, the proposed architecture can construct an input-output mapping in the form of fuzzy if-then rules. The performance of the new architecture is compared with an artificial neural networks controller and pure limited-rule fuzzy rule controller for the truck back-upper problem
Keywords
fuzzy control; fuzzy logic; genetic algorithms; learning systems; road vehicles; fuzzy logic controller; fuzzy rules generation; genetic algorithms; input-output mapping; learning procedure; system performance evaluator; truck back-upper problem; Artificial neural networks; Data mining; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Genetic algorithms; Genetic mutations; Humans;
fLanguage
English
Publisher
ieee
Conference_Titel
Robot and Human Communication, 1994. RO-MAN '94 Nagoya, Proceedings., 3rd IEEE International Workshop on
Conference_Location
Nagoya
Print_ISBN
0-7803-2002-6
Type
conf
DOI
10.1109/ROMAN.1994.365902
Filename
365902
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