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
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;
Conference_Titel :
Systems Engineering, 1992., IEEE International Conference on
Conference_Location :
Kobe
Print_ISBN :
0-7803-0734-8
DOI :
10.1109/ICSYSE.1992.236946
