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

โœ Scribed by A. Blanco; M. Delgado; M. C. Pegalajar

Publisher
John Wiley and Sons
Year
2000
Tongue
English
Weight
347 KB
Volume
15
Category
Article
ISSN
0884-8173

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โœฆ Synopsis

Although the extraction of symbolic knowledge from trained feedforward neural net-ลฝ . 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 ลฝ . 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 ลฝ . SOM for extracting knowledge from a recurrent neural network able to infer a ลฝ . crisprfuzzy regular language. Identification of this language is done only from a ลฝ . crisprfuzzy example set of the language.

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