Stochastic analysis of gradient adaptive identification of nonlinear systems with memory

Author

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

Author_Institution

Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA

Volume

3

fYear

1998

fDate

12-15 May 1998

Firstpage

1421

Abstract

Gradient search adaptive algorithm for identifying an unknown nonlinear system comprised of a discrete-time linear system H followed by a zero-memory nonlinearity g(.). The LMS algorithm first estimates H. The weights are then frozen. Recursions are derived for the mean and fluctuation behavior of LMS which agree with Monte Carte simulations. When the nonlinearity is modelled by a scaled error function, the second part of the gradient scheme is shown to correctly learn the scale factor and the error function scale factor. Mean recursions for the scale factors show good agreement with Monte Carlo simulations

Keywords

Wiener filters; adaptive filters; filtering theory; identification; least mean squares methods; nonlinear systems; search problems; stochastic processes; LMS algorithm; Monte Carlo simulations; discrete-time linear system; error function scale factor; gradient adaptive identification; gradient search adaptive algorithm; mean recursions; nonlinear systems; recursions derivation; scale factor; scaled error function; stochastic analysis; zero-memory nonlinearity; Adaptive algorithm; Additive noise; Algorithm design and analysis; Fluctuations; Laboratories; Least squares approximation; Linear systems; Neural networks; Nonlinear systems; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on

Conference_Location

Seattle, WA

ISSN

1520-6149

Print_ISBN

0-7803-4428-6

Type

conf

DOI

10.1109/ICASSP.1998.681714

Filename

681714