Constructive learning of recurrent neural network

D. Chen, C. L. Giles, G. Z. Sun, H. H. Chen, Y. C. Lee, M. W. Goudreau

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Recurrent neural networks are a natural model for learning and predicting temporal signals.In addition simple recurrent networks have been shown to be both theoretically and experimentally capable of learning finite state automata. However it is difficult to determine what is the minimal neural network structure for a particular automation. Using a large recurrent network, which would be versatile in theory in practice proves to be very difficult to train. Constructive or destructive recurrent methods might offer a solution to this problem. We prove that one current method. Recurrent cascade correlation has fundamental limitations in representation and thus in its learning capabilities. We give a preliminary approach on how to get around these limitation by devising a ″Simple″ constructive training method that adds neurons during training while still preserving the powerful fully recurrent structure. Through simulations we show that such a method can learn many types of regular grammars that the Recurrent Cascade Correlation method is unable to learn.

Original languageEnglish (US)
Title of host publication1993 IEEE International Conference on Neural Networks
PublisherPubl by IEEE
Number of pages10
ISBN (Print)0780312007
StatePublished - 1993
Event1993 IEEE International Conference on Neural Networks - San Francisco, California, USA
Duration: Mar 28 1993Apr 1 1993

Publication series

Name1993 IEEE International Conference on Neural Networks


Other1993 IEEE International Conference on Neural Networks
CitySan Francisco, California, USA

All Science Journal Classification (ASJC) codes

  • Engineering(all)


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