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 language | English (US) |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 803-808 |
| Number of pages | 6 |
| Volume | 1112 LNCS |
| ISBN (Print) | 3540615105, 9783540615101 |
| DOIs | |
| 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) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 1996 International Conference on Artificial Neural Networks, ICANN 1996 |
|---|---|
| Country | Germany |
| City | Bochum |
| Period | 7/16/96 → 7/19/96 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Approximation errors of state and output trajectories using recurrent neural networks. / Liu, Binfan; Si, Jennie.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1112 LNCS 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
}
TY - GEN
T1 - Approximation errors of state and output trajectories using recurrent neural networks
AU - Liu, Binfan
AU - Si, Jennie
PY - 1996
Y1 - 1996
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84902144167&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84902144167&partnerID=8YFLogxK
U2 - 10.1007/3-540-61510-5_135
DO - 10.1007/3-540-61510-5_135
M3 - Conference contribution
AN - SCOPUS:84902144167
SN - 3540615105
SN - 9783540615101
VL - 1112 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 803
EP - 808
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -