Transient Performance Analysis of the l1-RLS

被引：7

作者：

Gao, Wei ^{[1
]}

Chen, Jie ^{[2
]}

Richard, Cedric ^{[3
]}

Wentao, Shi ^{[2
]}

Zhang, Qunfei ^{[2
]}

机构：

[1] Jiangsu Univ, Sch Comp Sci & Telecommun Engn, Zhenjiang 212013, Jiangsu, Peoples R China

[2] Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian 710072, Peoples R China

[3] Univ Cote dAzur, CNRS, OCA, F-06108 Nice, France

来源：

IEEE SIGNAL PROCESSING LETTERS | 2022年 / 29卷

关键词：

Transient analysis; sparse system; online identification; l(1) -RLS; RECURSIVE LEAST-SQUARES; RLS; ALGORITHM; TESTS;

D O I：

10.1109/LSP.2021.3127467

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 recursive least-squares algorithm with l(1)-norm regularization (l(1) -RLS) exhibits excellent performance in terms of convergence rate and steady-state error in identification of sparse systems. Nevertheless few works have studied its stochastic behavior, in particular its transient performance. In this letter, we derive analytical models of the transient behavior of the l(1)-RLS in the mean and mean-square error sense. Simulation results illustrate the accuracy of these models.

引用

页码：90 / 94

页数：5

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