A neural expert system using fuzzy teaching input

Author

Hayashi, Yoichi

Author_Institution

Dept. of Comput. & Inf. Sci., Ibaraki Univ., Japan

fYear

1992

fDate

8-12 Mar 1992

Firstpage

485

Lastpage

491

Abstract

The author previously (1990, 1991) proposed a fuzzy neural expert system and provided a method to extract automatically fuzzy IF-THEN rules from a trained neural network. The previous work is extended and a neural expert system is proposed using fuzzy teaching input. The neural expert system can perform generalization of the information derived from training data with fuzzy teaching input and embodiment of knowledge in the form of a fuzzy neural network, where the fuzzy teaching input is subjectively given by domain experts: and extraction of fuzzy IF-THEN rules with linguistic relative importance of each proposition in an antecedent (IF-part) from a trained neural network. A method is proposed to extract automatically fuzzy IF-THEN rules from the trained neural network generated by training data with fuzzy teaching input

Keywords

expert systems; fuzzy logic; knowledge acquisition; neural nets; FNES; fuzzy IF-THEN rules; fuzzy neural expert system; fuzzy teaching input; information generalization; linguistic relative importance; rule extraction; Biological neural networks; Data mining; Education; Expert systems; Fuzzy neural networks; Fuzzy systems; Hybrid intelligent systems; Neural networks; Pediatrics; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1992., IEEE International Conference on

Conference_Location

San Diego, CA

Print_ISBN

0-7803-0236-2

Type

conf

DOI

10.1109/FUZZY.1992.258661

Filename

258661