A genetic-based method for learning the parameters of a fuzzy inference system

Author

Fagarasan, Florin ; Negoita, Mircea Gh

Author_Institution

Dept. of Fuzzy Syst., Neural Network & Soft Comput., Inst. of Microtechnol., Bucharest, Romania

fYear

1995

Firstpage

223

Lastpage

226

Abstract

Fuzzy inference systems (FIS) provide models for approximating continuous, real valued functions. The successful application of fuzzy reasoning models depends on a number of parameters, such as the fuzzy partition of the input/output universes of discourse, that are usually decided in a subjective manner (traditionally, fuzzy rule bases are constructed by knowledge acquisition from human experts). This paper presents a flexible genetic based method for learning the parameters of a FIS from examples such as the subjectivity not to be involved at all. We show that applying this method it is possible to obtain better performances for the FIS or, for the same performances, a less complex structure for the system

Keywords

fuzzy logic; genetic algorithms; inference mechanisms; knowledge acquisition; learning by example; uncertainty handling; fuzzy inference system; fuzzy reasoning models; genetic-based method; inductive learning; input/output universes; knowledge acquisition; learning; real valued functions; Computer networks; Databases; Electronic mail; Fuzzy control; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Knowledge acquisition; Neural networks; Optimized production technology;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Neural Networks and Expert Systems, 1995. Proceedings., Second New Zealand International Two-Stream Conference on

Conference_Location

Dunedin

Print_ISBN

0-8186-7174-2

Type

conf

DOI

10.1109/ANNES.1995.499476

Filename

499476