DocumentCode
306586
Title
Neural approximators for nonlinear sliding-window state observers
Author
Alessandri, A. ; Maggiore, M. ; Parisini, T. ; Zoppoli, R.
Author_Institution
Dept. of Commun. Comput. & Syst. Sci., Genoa Univ., Italy
Volume
2
fYear
1996
fDate
11-13 Dec 1996
Firstpage
1461
Abstract
State estimation on the basis of noisy measures is discussed, and a convergence condition for the state estimate is given. Neural approximation of the nonlinear observer is then considered, and the method is briefly compared with the extended Kalman filter method
Keywords
approximation theory; neural nets; noise; nonlinear systems; observers; convergence condition; extended Kalman filter; neural approximation; noisy measures; nonlinear sliding-window state observers; state estimation; Additive noise; Control systems; Electric variables measurement; Length measurement; Noise measurement; Nonlinear control systems; Nonlinear dynamical systems; Observability; Observers; State estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1996., Proceedings of the 35th IEEE Conference on
Conference_Location
Kobe
ISSN
0191-2216
Print_ISBN
0-7803-3590-2
Type
conf
DOI
10.1109/CDC.1996.572720
Filename
572720
Link To Document