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 |
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ASJC Scopus subject areas
- Control and Systems Engineering
Cite this
Control relevant long-range plant identification using recurrent neural networks. / Si, Jennie; Zhou, Guian.
Proceedings of the American Control Conference. Vol. 1 1995. p. 857-861.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Control relevant long-range plant identification using recurrent neural networks
AU - Si, Jennie
AU - Zhou, Guian
PY - 1995
Y1 - 1995
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0029202785&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0029202785&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0029202785
VL - 1
SP - 857
EP - 861
BT - Proceedings of the American Control Conference
ER -