Title :
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
fDate :
6/1/2000 12:00:00 AM
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;
Journal_Title :
Signal Processing, IEEE Transactions on
