Analysis of stochastic gradient identification of polynomial nonlinear systems with memory

Author

Celka, P. ; Bershad, N.J. ; Vesin, J.M.

Author_Institution

Dept. of Electr. Eng., Fed. Inst. of Technol., Zurich, Switzerland

Volume

3

fYear

1999

fDate

15-19 Mar 1999

Firstpage

1293

Abstract

This paper presents analytical, numerical and experimental results for a stochastic gradient adaptive scheme which identifies a polynomial-type nonlinear system with memory for noisy output observations. The analysis includes the computation of the stationary points, the mean square error surface, and the mean behaviour of the algorithm for Gaussian data. Monte Carlo simulations confirm the theoretical predictions which show a small sensitivity to the observation noise

Keywords

Monte Carlo methods; Wiener filters; adaptive filters; gradient methods; identification; least mean squares methods; nonlinear systems; polynomials; stochastic processes; Gaussian data; Monte Carlo simulations; adaptive scheme; mean behaviour; mean square error surface; memory; noisy output observations; observation noise; polynomial nonlinear systems; stationary points; stochastic gradient identification; Algorithm design and analysis; Least squares approximation; Mean square error methods; Nonlinear filters; Nonlinear systems; Polynomials; Signal analysis; Signal processing algorithms; Signal to noise ratio; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on

Conference_Location

Phoenix, AZ

ISSN

1520-6149

Print_ISBN

0-7803-5041-3

Type

conf

DOI

10.1109/ICASSP.1999.756216

Filename

756216