An algorithm based on the even moments of the error

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 1900-01-01 DOI:10.1109/NNSP.2003.1318087

A. Barros, J. Príncipe, Y. Takeuchi, C. H. Sales, N. Ohnishi

引用次数: 10

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.

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一种基于误差偶矩的算法

提出了一种基于误差偶矩的线性组合进行自适应滤波的算法，称为加权偶矩(WEM)算法。它类似于众所周知的最小均方(LMS)和Walach和Widrow(1994)提出的一系列算法。然而，当噪声是高斯噪声时，后一种方法的表现不如LMS。研究了WEM算法的收敛性，推导了误差和学习时间的方程。结果表明，当噪声为高斯分布时，WEM的性能优于LMS。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)

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