BPEXS: a learning rule for expert systems

Author

Happee, S.E.T. ; Jager, R. ; Verbruggen, H.B.

Author_Institution

Dept. of Electr. Eng., Delft Univ. of Technol., Netherlands

fYear

1992

fDate

11-13 Aug 1992

Firstpage

374

Lastpage

378

Abstract

The BPEXS learning algorithm for expert systems is presented. It adapts the confidence factors of a fuzzy reasoning expert system, using an algorithm based on the backpropagation learning rule for neural networks. The BPEXS algorithm was designed in a very general way. It can be applied to any expert system, provided that the product operator is chosen to implement the generalized modus ponens and the expert system´s conclusions are defuzzified using the center-of-area method. Preliminary results show that the BPEXS algorithm can adapt the knowledge of an expert system to identify various second-order processes with reasonable accuracy

Keywords

backpropagation; expert systems; fuzzy logic; inference mechanisms; uncertainty handling; BPEXS; backpropagation; center-of-area method; confidence factors; expert systems; fuzzy reasoning; learning rule; modus ponens; Algorithm design and analysis; Artificial intelligence; Artificial neural networks; Backpropagation algorithms; Convergence; Expert systems; Fuzzy reasoning; Laboratories; Neural networks; Signal generators;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control, 1992., Proceedings of the 1992 IEEE International Symposium on

Conference_Location

Glasgow

ISSN

2158-9860

Print_ISBN

0-7803-0546-9

Type

conf

DOI

10.1109/ISIC.1992.225120

Filename

225120