Title :
A new approach to genetics based machine learning in fuzzy controller design
Author :
Carse, Brian ; Fogarty, Terence C.
Author_Institution :
Univ. of the West of England, Bristol, UK
Abstract :
This paper proposes an evolutionary approach to fuzzy controller design based on the “Pittsburgh” style classifier system in which whole rule-sets are the unit of credit assignment and selection. We present a description of a system based on this idea, together with experimental results using the system to learn function identification. The representation used allows the genetic algorithm to vary both membership functions (centres and widths) and fuzzy relations. We introduce a new crossover operator which employs crosspoints in the input space and demonstrate its efficacy. Finally, we present results which show that the classifier system is capable of self-organisation of membership functions and fuzzy relations simultaneously
Keywords :
fuzzy control; genetic algorithms; identification; learning (artificial intelligence); Pittsburgh style classifier; classifier system; function identification; fuzzy controller; genetic algorithm; machine learning; membership functions; rule-sets; self-organisation; Automatic control; Environmental economics; Fuzzy control; Fuzzy set theory; Fuzzy sets; Fuzzy systems; Genetics; Machine learning; Motion control; Temperature control;
Conference_Titel :
Intelligent Control, 1994., Proceedings of the 1994 IEEE International Symposium on
Conference_Location :
Columbus, OH
Print_ISBN :
0-7803-1990-7
DOI :
10.1109/ISIC.1994.367812