Title :
Nonlinear state estimation using neural filters
Author :
Haykin, Simon ; Yee, Paul
Author_Institution :
Commun. Res. Lab., McMaster Univ., Hamilton, Ont., Canada
Abstract :
In this paper we present procedures for estimating the state of a nonlinear dynamical system, which are based on the use of radial basis function (RBF) networks. Experimental results are presented, which compare the performance of this approach with that of J.T. Lo published in 1995
Keywords :
adaptive filters; feedforward neural nets; filtering theory; nonlinear dynamical systems; nonlinear filters; state estimation; state-space methods; RBF networks; neural filters; nonlinear dynamical system; radial basis function networks; state estimation; state-space model; Adaptive filters; Additive noise; Filtering; Multilayer perceptrons; Nonlinear dynamical systems; Nonlinear equations; Nonlinear filters; Signal processing algorithms; State estimation; Wiener filter;
Conference_Titel :
Military Communications Conference, 1996. MILCOM '96, Conference Proceedings, IEEE
Conference_Location :
McLean, VA
Print_ISBN :
0-7803-3682-8
DOI :
10.1109/MILCOM.1996.569416
