An algorithm based on the even moments of the error

Author

Barros, Allan Kardec ; Principe, José ; Takeuchi, Yoshinori ; Sales, Carlos H. ; Ohnishi, Noboru

Author_Institution

Univ. Fed. do Maranhao, Brazil

fYear

2003

fDate

17-19 Sept. 2003

Firstpage

879

Lastpage

885

Abstract

We propose an algorithm based on a linear combination of the even moments of the error for adaptive filtering, called weighted even moment (WEM) algorithm. It is similar to the well-known least mean square (LMS) and to the family of algorithms proposed by Walach and Widrow (1994). This later ones were shown to behave poorer than the LMS, however, when the noise was Gaussian. We study the WEM algorithm convergence behavior and deduce equations for the misadjustment and the learning time. The results showed that the WEM had better performance than the LMS when the noise had a Gaussian distribution.

Keywords

Gaussian noise; adaptive filters; adaptive signal processing; convergence; error analysis; least mean squares methods; Gaussian noise; adaptive filtering; convergence behavior; even moments; least mean square; linear combination; weighted even moment algorithm; Adaptive filters; Algorithm design and analysis; Convergence; Equations; Gaussian distribution; Gaussian noise; Higher order statistics; Least squares approximation; Signal processing algorithms; Statistical distributions;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing, 2003. NNSP'03. 2003 IEEE 13th Workshop on

ISSN

1089-3555

Print_ISBN

0-7803-8177-7

Type

conf

DOI

10.1109/NNSP.2003.1318087

Filename

1318087