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

Binfan Liu; Jennie Si

doi:10.1109/81.641775

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

Binfan Liu, Jennie Si

Electrical Engineering

Research output: Contribution to journal › Article › peer-review

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 ∈ R^m.

Original language	English (US)
Pages (from-to)	1086-1089
Number of pages	4
Journal	IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications
Volume	44
Issue number	11
DOIs	https://doi.org/10.1109/81.641775
State	Published - 1997

Keywords

Modeling error bounds
Nonlinear dynamic system modeling
Recurrent neural networks

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/81.641775

Cite this

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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.",

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

Abstract

Keywords

ASJC Scopus subject areas

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