Interpretation of artificial neural networks by means of fuzzy rules

Author

Castro, Juan L. ; Mantas, Carlos J. ; Benítez, José M.

Author_Institution

Dept. of Comput. Sci. & Artificial Intelligence, Granada Univ., Spain

Volume

13

Issue

1

fYear

2002

fDate

1/1/2002 12:00:00 AM

Firstpage

101

Lastpage

116

Abstract

This paper presents an extension of the method presented by Benitez et al (1997) for extracting fuzzy rules from an artificial neural network (ANN) that express exactly its behavior. The extraction process provides an interpretation of the ANN in terms of fuzzy rules. The fuzzy rules presented are in accordance with the domain of the input variables. These rules use a new operator in the antecedent. The properties and intuitive meaning of this operator are studied. Next, the role of the biases in the fuzzy rule-based systems is analyzed. Several examples are presented to comment on the obtained fuzzy rule-based systems. Finally, the interpretation of ANNs with two or more hidden layers is also studied

Keywords

feedforward neural nets; fuzzy systems; knowledge acquisition; knowledge based systems; feedforward neural network; fuzzy rule extraction; fuzzy systems; multilayer neural network; rule-based systems; Artificial intelligence; Artificial neural networks; Computer science; Fuzzy neural networks; Fuzzy systems; Input variables; Iris; Knowledge based systems; Parallel processing; Performance analysis;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.977279

Filename

977279