Approximation errors of state and output trajectories using recurrent neural networks

Binfan Liu; Jennie Si

doi:10.1007/3-540-61510-5_135

Approximation errors of state and output trajectories using recurrent neural networks

Binfan Liu, Jennie Si

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 ∈ R^m.

Original language	English (US)
Title of host publication	Artificial Neural Networks, ICANN 1996 - 1996 International Conference, Proceedings
Publisher	Springer Verlag
Pages	803-808
Number of pages	6
ISBN (Print)	3540615105, 9783540615101
DOIs	https://doi.org/10.1007/3-540-61510-5_135
State	Published - 1996
Event	1996 International Conference on Artificial Neural Networks, ICANN 1996 - Bochum, Germany Duration: Jul 16 1996 → Jul 19 1996

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1112 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	1996 International Conference on Artificial Neural Networks, ICANN 1996
Country/Territory	Germany
City	Bochum
Period	7/16/96 → 7/19/96

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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10.1007/3-540-61510-5_135

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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

Approximation errors of state and output trajectories using recurrent neural networks. / Liu, Binfan; Si, Jennie.

Artificial Neural Networks, ICANN 1996 - 1996 International Conference, Proceedings. Springer Verlag, 1996. p. 803-808 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1112 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1112 LNCS, Springer Verlag, pp. 803-808, 1996 International Conference on Artificial Neural Networks, ICANN 1996, Bochum, Germany, 7/16/96. https://doi.org/10.1007/3-540-61510-5_135

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

Liu, Binfan ; Si, Jennie. / Approximation errors of state and output trajectories using recurrent neural networks. Artificial Neural Networks, ICANN 1996 - 1996 International Conference, Proceedings. Springer Verlag, 1996. pp. 803-808 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Approximation errors of state and output trajectories using recurrent neural networks

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