Abstract
In adaptive critic control systems design as well as other control systems design schemes, e.g., model-based predictive control, the plant model has to be iterated to predict many time steps ahead into the future. A commonly used implementation is to employ a parallel identification structure. It has been justified for linear estimation that (assume that estimates to the real plant are biased) long-range prediction models are less sensitive to high frequency noise, whether actual noise or caused by model-plant mismatch. We address feasibilities of using recurrent neural networks for long-range plant identification. We examine the existence, training and performance of such recurrent neural network identifiers.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the American Control Conference |
| Pages | 857-861 |
| Number of pages | 5 |
| Volume | 1 |
| State | Published - 1995 |
| Event | Proceedings of the 1995 American Control Conference. Part 1 (of 6) - Seattle, WA, USA Duration: Jun 21 1995 → Jun 23 1995 |
Other
| Other | Proceedings of the 1995 American Control Conference. Part 1 (of 6) |
|---|---|
| City | Seattle, WA, USA |
| Period | 6/21/95 → 6/23/95 |
ASJC Scopus subject areas
- Control and Systems Engineering
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Cite this
Si, J., & Zhou, G. (1995). Control relevant long-range plant identification using recurrent neural networks. In Proceedings of the American Control Conference (Vol. 1, pp. 857-861)