A stochastic model for a pseudo affine projection algorithm operating in a nonstationary environment

Author

De Almeida, Sérgio J M ; Bershad, Neil J. ; Bermudez, José C M

Author_Institution

Catholic Univ. of Pelotas, Brazil

Volume

1

fYear

2004

fDate

7-10 Nov. 2004

Firstpage

246

Abstract

This paper presents a statistical analysis of a pseudo affine projection (PAP) algorithm, obtained from the affine projection algorithm (AP) for a step size α<1 and a scalar error signal in the weight update. Deterministic recursive equations are derived for the mean weight and for the mean square error for a large number of adaptive taps N compared to the order P of the algorithm. Simulations are presented which show excellent agreement with the theory in the transient and steady states. The PAP learning behavior is of special interest in applications where tradeoffs are necessary between convergence speed and steady-state misadjustment.

Keywords

Monte Carlo methods; convergence; mean square error methods; signal processing; statistical analysis; stochastic processes; PAP learning behavior; affine projection algorithm; convergence; deterministic recursive equation; mean square error; pseudo affine projection algorithm; statistical analysis; steady state misadjustment; stochastic model; transient state; Convergence; Equations; Error correction; Mean square error methods; Noise reduction; Projection algorithms; Semiconductor device noise; Statistical analysis; Steady-state; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on

Print_ISBN

0-7803-8622-1

Type

conf

DOI

10.1109/ACSSC.2004.1399129

Filename

1399129