Extracting rules from a (fuzzy/crisp) recurrent neural network using a self-organizing map

Although the extraction of symbolic knowledge from trained feedforward neural netŽ . works 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 Ž . crisprfuzzy 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 Ž . SOM for extracting knowledge from a recurrent neural network able to infer a Ž . crisprfuzzy regular language. Identification of this language is done only from a Ž . crisprfuzzy example set of the language. Q 2000 John Wiley & Sons, Inc.