DocumentCode
3472513
Title
A neural-network-based system identification technique
Author
Stubberud, A. ; Wabgaonkar, H. ; Stubberud, S.
Author_Institution
California Univ., Irvine, CA, USA
fYear
1991
fDate
11-13 Dec 1991
Firstpage
869
Abstract
A dynamic system identification technique based on neural networks is presented. The key idea is that the static training techniques can be applied to the dynamic case as well, provided the system is represented by a nonlinear state-space model. The feedforward-type neural network was trained using an extended Kalman filter to capture the input-output characteristics of a dynamic system
Keywords
Kalman filters; feedforward neural nets; filtering and prediction theory; identification; learning (artificial intelligence); nonlinear systems; state-space methods; dynamic system identification; extended Kalman filter; feedforward-type neural network; input-output characteristics; neural-network-based system identification technique; nonlinear state-space model; static training techniques; Approximation algorithms; Artificial neural networks; Difference equations; Feedforward neural networks; Neural networks; Neurons; Nonlinear dynamical systems; Nonlinear equations; State estimation; System identification; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1991., Proceedings of the 30th IEEE Conference on
Conference_Location
Brighton
Print_ISBN
0-7803-0450-0
Type
conf
DOI
10.1109/CDC.1991.261441
Filename
261441
Link To Document