Adaptive filters based on the high order error statistics

Author

Cho, Sungho ; Kim, SangDuck

Author_Institution

Dept. of Electron. Eng., Hanyang Univ., Ansan, South Korea

fYear

1996

fDate

18-21 Nov 1996

Firstpage

109

Lastpage

112

Abstract

This paper presents convergence analyses of the stochastic gradient adaptive algorithms based on high order error power criteria. In particular, our attention has focused on investigating the statistical behaviour of the least mean absolute third (LMAT) and the least mean fourth (LMF) adaptive algorithms. For each algorithm, under a set of mild assumptions, we have derived nonlinear evolution equations that characterize the mean and mean-squared behaviour of the algorithm. Computer simulation examples show fairly good agreement between the theoretical and actual behaviour of the two algorithms

Keywords

adaptive filters; adaptive signal processing; circuit analysis computing; convergence of numerical methods; error analysis; higher order statistics; least mean squares methods; nonlinear equations; adaptive filters; computer simulation examples; convergence analyses; high order error power criteria; high order error statistics; least mean absolute third adaptive algorithm; least mean fourth adaptive algorithm; nonlinear evolution equations; statistical behaviour; stochastic gradient adaptive algorithms; Adaptive algorithm; Adaptive filters; Algorithm design and analysis; Convergence; Error analysis; Finite impulse response filter; Least squares approximation; Nonlinear equations; Signal processing algorithms; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1996., IEEE Asia Pacific Conference on

Conference_Location

Seoul

Print_ISBN

0-7803-3702-6

Type

conf

DOI

10.1109/APCAS.1996.569231

Filename

569231