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 language | English (US) |
|---|---|
| Title of host publication | Proceedings of the IEEE Conference on Decision and Control |
| Publisher | IEEE |
| Pages | 1574-1578 |
| Number of pages | 5 |
| Volume | 2 |
| State | Published - 1997 |
| Event | Proceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) - San Diego, CA, USA Duration: Dec 10 1997 → Dec 12 1997 |
Other
| Other | Proceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) |
|---|---|
| City | San Diego, CA, USA |
| Period | 12/10/97 → 12/12/97 |
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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.