On the approximation capability of neural networks-dynamic system modeling and control
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 122-130 |
| Journal / Publication | Asian Journal of Control |
| Volume | 3 |
| Issue number | 2 |
| Publication status | Published - Jun 2001 |
Link(s)
Abstract
This paper discusses issues related to the approximation capability of neural networks in modeling and control. We show that neural networks are universal models and universal controllers for a class of nonlinear dynamic systems. That is, for a given dynamic system, there exists a neural network which can model the system to any degree of accuracy over time. Moreover, if the system to be controlled is stabilized by a continuous controller, then there exists a neural network which can approximate the controller such that the system controlled by the neural network is also stabilized with a given bound of output error.
Research Area(s)
- Neural networks, Systems modeling, Universal controllers
Citation Format(s)
On the approximation capability of neural networks-dynamic system modeling and control. / Chak, C. K.; Feng, G.; Ma, J.
In: Asian Journal of Control, Vol. 3, No. 2, 06.2001, p. 122-130.
In: Asian Journal of Control, Vol. 3, No. 2, 06.2001, p. 122-130.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
