A noise constrained least mean fourth (NCLMF) adaptive algorithm

被引：11

作者：

Zerguine, Azzedine ^{[1
]}

Moinuddin, Muhammad ^{[1
]}

Imam, Syed Ali Aamir ^{[1
]}

机构：

[1] King Fahd Univ Petr & Minerals, Dept Elect Engn, Dhahran 31261, Saudi Arabia

来源：

SIGNAL PROCESSING | 2011年 / 91卷 / 01期

关键词：

Adaptive filters; LMS; LMF; Constrained optimization; Noise constraints; NCLMF algorithm; TRACKING ANALYSIS; CONVERGENCE;

D O I：

10.1016/j.sigpro.2010.06.020

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The learning speed of an adaptive algorithm can be improved by properly constraining the cost function of the adaptive algorithm. In this work, a noise-constrained least mean fourth (NCLMF) adaptive algorithm is proposed. The NCLMF algorithm is obtained by constraining the cost function of the standard LMF algorithm to the fourth-order moment of the additive noise. The NCLMF algorithm can be seen as a variable step-size LMF algorithm. The main aim of this work is to derive the NCLMF adaptive algorithm, analyze its convergence behavior, and assess its performance in different noise environments. Furthermore, the analysis of the proposed NCLMF algorithm is carried Out using the concept of energy conservation. Finally, a number of simulation results are carried out to corroborate the theoretical findings, and as expected, improved performance is obtained through the use of this technique over the traditional LMF algorithm. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：136 / 149

页数：14

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