Approximation errors of state and output trajectories using recurrent neural networks

Binfan Liu, Jennie Si

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

Abstract

This paper addresses the problem of estimating training error bounds of state and output trajectories for a class of recurrent neural networks as models of nonlinear dynamic systems. We present training error bounds of trajectories between the recurrent neural network models and the target systems. The bounds are obtained provided that the models have been trained on N trajectories with N independent random initial values which are uniformly distributed over [a, b]m ∈ Rm.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks, ICANN 1996 - 1996 International Conference, Proceedings
PublisherSpringer Verlag
Pages803-808
Number of pages6
ISBN (Print)3540615105, 9783540615101
DOIs
StatePublished - Jan 1 1996
Event1996 International Conference on Artificial Neural Networks, ICANN 1996 - Bochum, Germany
Duration: Jul 16 1996Jul 19 1996

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1112 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1996 International Conference on Artificial Neural Networks, ICANN 1996
CountryGermany
CityBochum
Period7/16/967/19/96

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Liu, B., & Si, J. (1996). Approximation errors of state and output trajectories using recurrent neural networks. In Artificial Neural Networks, ICANN 1996 - 1996 International Conference, Proceedings (pp. 803-808). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1112 LNCS). Springer Verlag. https://doi.org/10.1007/3-540-61510-5_135