Dynamic recurrent neural networks: theory and applications

C. Lee Giles, Gary M. Kuhn, Ronald J. Williams

Research output: Contribution to journalArticle

53 Citations (Scopus)

Abstract

Illustrated in this paper are the scientific trends of the early work in recurrent neural networks and the mathematics of training when at least some recurrent terms of the network derivatives can be non-zero.

Original languageEnglish (US)
Pages (from-to)153-156
Number of pages4
JournalIEEE Transactions on Neural Networks
Volume5
Issue number2
StatePublished - Mar 1994

Fingerprint

Dynamic Neural Networks
Recurrent neural networks
Circuit theory
Recurrent Neural Networks
Derivatives
Derivative
Term
Training
Trends

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Hardware and Architecture
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Theoretical Computer Science

Cite this

Giles, C. Lee ; Kuhn, Gary M. ; Williams, Ronald J. / Dynamic recurrent neural networks : theory and applications. In: IEEE Transactions on Neural Networks. 1994 ; Vol. 5, No. 2. pp. 153-156.
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Dynamic recurrent neural networks : theory and applications. / Giles, C. Lee; Kuhn, Gary M.; Williams, Ronald J.

In: IEEE Transactions on Neural Networks, Vol. 5, No. 2, 03.1994, p. 153-156.

Research output: Contribution to journalArticle

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