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Extracting rules from a (fuzzy/crisp) recurrent neural network using a self‐organizing map
Author(s) -
Blanco A.,
Delgado M.,
Pegalajar M. C.
Publication year - 2000
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(200007)15:7<595::aid-int2>3.0.co;2-5
Subject(s) - computer science , artificial intelligence , neuro fuzzy , artificial neural network , recurrent neural network , feedforward neural network , fuzzy logic , identification (biology) , set (abstract data type) , adaptive neuro fuzzy inference system , grammar induction , time delay neural network , fuzzy set , self organizing map , machine learning , fuzzy control system , rule based machine translation , botany , biology , programming language
Abstract Although the extraction of symbolic knowledge from trained feedforward neural networks has been widely studied, research in recurrent neural networks (RNN) has been more neglected, even though it performs better in areas such as control, speech recognition, time series prediction, etc. Nowadays, a subject of particular interest is (crisp/fuzzy) grammatical inference, in which the application of these neural networks has proven to be suitable. In this paper, we present a method using a self‐organizing map (SOM) for extracting knowledge from a recurrent neural network able to infer a (crisp/fuzzy) regular language. Identification of this language is done only from a (crisp/fuzzy) example set of the language. © 2000 John Wiley & Sons, Inc.