Fluctuation analysis of stochastic gradient identification of polynomial Wiener systems

Author

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

Author_Institution

Signal Process. Res. Centre, Queensland Univ. of Technol., Brisbane, Qld., Australia

Volume

48

Issue

6

fYear

2000

fDate

6/1/2000 12:00:00 AM

Firstpage

1820

Lastpage

1825

Abstract

This correspondence presents analytical results and Monte Carlo simulations for the fluctuation behavior of a stochastic gradient adaptive identification scheme. This scheme identifies a polynomial Wiener system (linear FIR filter followed by a static polynomial nonlinearity) for noisy output observations. The analysis includes (1) bounds and a recursion for the misadjustment matrix and (2) algorithm mean square stability regions. A diagonal step-size matrix for the adaptive coefficients is introduced to speed up convergence. The theoretical predictions of the fluctuation analysis are supported by Monte Carlo simulations

Keywords

FIR filters; Monte Carlo methods; Wiener filters; adaptive signal processing; convergence of numerical methods; fluctuations; gradient methods; identification; matrix algebra; polynomials; stochastic processes; Monte Carlo simulations; adaptive coefficients; adaptive identification scheme; algorithm mean square stability regions; bounds; convergence; diagonal step-size matrix; fluctuation analysis; linear FIR filter; misadjustment matrix; noisy output observations; polynomial Wiener system; polynomial Wiener systems; recursion; static polynomial nonlinearity; stochastic gradient identification; Convergence; Covariance matrix; Finite impulse response filter; Fluctuations; Polynomials; Signal processing algorithms; Stability analysis; Stochastic processes; Stochastic resonance; Stochastic systems;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.845945

Filename

845945