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

Binfan Liu, Jennie Si

Research output: Contribution to journalArticle

Abstract

This letter 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 ∈ Rm.

Original languageEnglish (US)
Pages (from-to)1086-1089
Number of pages4
JournalIEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
Volume44
Issue number11
DOIs
StatePublished - Dec 1 1997

Keywords

  • Modeling error bounds
  • Nonlinear dynamic system modeling
  • Recurrent neural networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Error estimation of recurrent neural network models trained on a finite set of initial values'. Together they form a unique fingerprint.

  • Cite this