A neuro-fuzzy-GA system architecture for helping the knowledge acquisition process

Author

Brasil, L.M. ; De Azevedo, F.M. ; Barreto, J.M. ; Noirhomme-Fraiture, Monique

Author_Institution

Dept. of Electr. Eng., Univ. Fed. de Santa Catarina, Florianapolis, Brazil

fYear

1998

fDate

21-23 May 1998

Firstpage

57

Lastpage

64

Abstract

The knowledge acquisition process consists on extracting knowledge of a domain expert. This work aims to minimize the intrinsic difficulties of the knowledge acquisition process. For achieve this purpose, all possible rules from the domain expert and a set of example were obtained for a short time interval. The proposed hybrid expert system minimizes the knowledge acquisition difficulties using a new methodology. To build this hybrid architecture, several tools were used: symbolic paradigm, connectionist paradigm, fuzzy logic and genetic algorithm

Keywords

expert systems; fuzzy set theory; genetic algorithms; knowledge acquisition; neural net architecture; connectionist paradigm; domain expert; fuzzy logic; genetic algorithm; hybrid expert system; intrinsic difficulty minimization; knowledge acquisition process; knowledge extraction; neuro-fuzzy-GA system architecture; symbolic paradigm; Artificial neural networks; Computational intelligence; Computer architecture; Expert systems; Fuzzy logic; Humans; Hybrid intelligent systems; Knowledge acquisition; Knowledge representation; Neurons;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligence and Systems, 1998. Proceedings., IEEE International Joint Symposia on

Conference_Location

Rockville, MD

Print_ISBN

0-8186-8548-4

Type

conf

DOI

10.1109/IJSIS.1998.685417

Filename

685417