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 ore presented, which compare the performance of this approach with that of J.T. Lo (1995).
Keywords :
adaptive filters; neural nets; nonlinear dynamical systems; nonlinear filters; state estimation; neural filters; nonlinear dynamical system; nonlinear state estimation; performance; radial basis function; Adaptive filters; Additive noise; Filtering; Multilayer perceptrons; Nonlinear dynamical systems; Nonlinear equations; Nonlinear filters; Signal processing algorithms; State estimation; Wiener filter;
Conference_Titel :
Signals, Systems and Computers, 1996. Conference Record of the Thirtieth Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-8186-7646-9
DOI :
10.1109/ACSSC.1996.600935
