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An Analytically Transparent Network for Sequence Recognition

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ICANN ’93 (ICANN 1993)

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Abstract

In this article technical details of a neural network for sequence recognition are presented. The network is powerful enough to simulate finite-state acceptors, while its analysis is much simplified, compared to the standard Hopfield model. Also its processing speed is optimal, and the presence of mixture states is externally controllable. The network is robust under synaptic noise, it has error correcting properties and it can recover from gross input errors.

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Author information

Authors and Affiliations

  1. Computer Science Dept., University of Twente, P.O Box 217, 7500 AE, Enschede, The Netherlands

    Marc F. J. Drossaers

Authors
  1. Marc F. J. Drossaers
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Editor information

Editors and Affiliations

  1. Dutch Foundation for Neural Networks, University of Nijmegen, Geert Grooteplein 21, 6525 EZ, Nijmegen, The Netherlands

    Stan Gielen  & Bert Kappen  & 

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© 1993 Springer-Verlag London Limited

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Drossaers, M.F.J. (1993). An Analytically Transparent Network for Sequence Recognition. In: Gielen, S., Kappen, B. (eds) ICANN ’93. ICANN 1993. Springer, London. https://doi.org/10.1007/978-1-4471-2063-6_94

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  • DOI: https://doi.org/10.1007/978-1-4471-2063-6_94

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  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19839-0

  • Online ISBN: 978-1-4471-2063-6

  • eBook Packages: Springer Book Archive

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