Transient performance analysis of zero-attracting LMS

Jie Chen, Cedric Richard, Yingying Song, David Brie

科研成果: 期刊稿件文章同行评审

34 引用 (Scopus)

摘要

Zero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for online sparse system identification. It combines the LMS framework and ℓ1-norm regularization to promote sparsity, and relies on subgradient iterations. Despite the significant interest in ZA-LMS, few works analyzed its transient behavior. The main difficulty lies in the nonlinearity of the update rule. In this study, a detailed analysis in the mean and mean-square sense is carried out in order to examine the behavior of the algorithm. Simulation results illustrate the accuracy of the model and highlight its performance through comparisons with an existing model.

源语言英语
文章编号7589076
页(从-至)1786-1790
页数5
期刊IEEE Signal Processing Letters
23
12
DOI
出版状态已出版 - 12月 2016

指纹

探究 'Transient performance analysis of zero-attracting LMS' 的科研主题。它们共同构成独一无二的指纹。

引用此