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
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;
Conference_Titel :
Decision and Control, 1996., Proceedings of the 35th IEEE Conference on
Conference_Location :
Kobe
Print_ISBN :
0-7803-3590-2
DOI :
10.1109/CDC.1996.572720
