Error estimation of recurrent neural network models trained on a finite set of initial values

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. 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 ∈R m.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Conference on Decision and Control
PublisherIEEE
Pages1574-1578
Number of pages5
Volume2
StatePublished - 1997
EventProceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) - San Diego, CA, USA
Duration: Dec 10 1997Dec 12 1997

Other

OtherProceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5)
CitySan Diego, CA, USA
Period12/10/9712/12/97

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ASJC Scopus subject areas

  • Chemical Health and Safety
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality

Cite this

Liu, B., & Si, J. (1997). Error estimation of recurrent neural network models trained on a finite set of initial values. In Proceedings of the IEEE Conference on Decision and Control (Vol. 2, pp. 1574-1578). IEEE.