Convergence and steady-state analysis of the normalized least mean fourth algorithm

被引：57
|
作者：
Zerguine, Azzedine [1 ]
机构：
[1] King Fahd Univ Petr & Minerals, Dept Elect Engn, Dhahran 31261, Saudi Arabia
来源：
DIGITAL SIGNAL PROCESSING | 2007年 / 17卷 / 01期
关键词：
LMS algorithm; NLMS algorithm; LMF algorithm; NLMF algorithm;
D O I：
10.1016/j.dsp.2006.01.005
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 normalized least mean-fourth (NLMF) algorithm is presented in this work and shown to have potentially faster convergence. Unlike the LMF algorithm, the convergence behavior of the NLMF algorithm is independent of the input data correlation statistics. Sufficient conditions for the NLMF algorithm convergence in the mean are obtained and an analysis of the steady-state performance is carried out with a new approach. The latter uses the concept of feedback and bypasses the need for working directly with the weight error covariance matrix. Simulation results obtained in a system identification scenario confirms the theoretical predictions on performance of the NLMF algorithm. (c) 2006 Elsevier Inc. All rights reserved.
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页码：17 / 31
页数：15
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