DocumentCode
3315061
Title
Neural strategies for nonlinear optimal filtering
Author
Alessandri, A. ; Parisini, T. ; Sanguineti, M. ; Zoppoli, R.
Author_Institution
Dept. of Commun., Comput. & Syst. Sci., Genoa Univ., Italy
fYear
1992
fDate
17-19 Sep 1992
Firstpage
44
Lastpage
49
Abstract
An approach to the solution of the optimal filtering problem by means of neural networks is proposed. It is a nonlinear filtering method using feedforward neural networks. In comparison with classical methods, like the extended Kalman filter, the approach involves no linearization, and requires no strong prior assumptions about the statistical properties of the random noises acting on both the dynamic system and the observation channel
Keywords
feedforward neural nets; filtering and prediction theory; extended Kalman filter; feedforward neural networks; nonlinear optimal filtering; Communication system control; Equations; Filtering; Multi-layer neural network; Neural networks; Noise measurement; Nonlinear control systems; Nonlinear filters; Stochastic processes; Stochastic systems;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems Engineering, 1992., IEEE International Conference on
Conference_Location
Kobe
Print_ISBN
0-7803-0734-8
Type
conf
DOI
10.1109/ICSYSE.1992.236946
Filename
236946
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