Transient Performance Analysis of the ℓ1-RLS

Wei Gao; Jie Chen; Cedric Richard; Shi Wentao; Qunfei Zhang

doi:10.1109/LSP.2021.3127467

Transient Performance Analysis of the ℓ₁-RLS

Wei Gao, Jie Chen, Cedric Richard, Shi Wentao, Qunfei Zhang

航海学院

科研成果: 期刊稿件 › 文章 › 同行评审

7 引用（Scopus）

摘要

The recursive least-squares algorithm with ℓ1-norm regularization (ℓ1-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 ℓ1-RLS in the mean and mean-square error sense. Simulation results illustrate the accuracy of these models.

源语言	英语
页（从-至）	90-94
页数	5
期刊	IEEE Signal Processing Letters
卷	29
DOI	https://doi.org/10.1109/LSP.2021.3127467
出版状态	已出版 - 2022

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abstract = "The recursive least-squares algorithm with ℓ1-norm regularization (ℓ1-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 ℓ1-RLS in the mean and mean-square error sense. Simulation results illustrate the accuracy of these models.",

keywords = "online identification, sparse system, Transient analysis, ℓ-RLS",

author = "Wei Gao and Jie Chen and Cedric Richard and Shi Wentao and Qunfei Zhang",

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N2 - The recursive least-squares algorithm with ℓ1-norm regularization (ℓ1-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 ℓ1-RLS in the mean and mean-square error sense. Simulation results illustrate the accuracy of these models.

AB - The recursive least-squares algorithm with ℓ1-norm regularization (ℓ1-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 ℓ1-RLS in the mean and mean-square error sense. Simulation results illustrate the accuracy of these models.

KW - online identification

KW - sparse system

KW - Transient analysis

KW - ℓ-RLS

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JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

ER -

Transient Performance Analysis of the ℓ1-RLS

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Transient Performance Analysis of the ℓ₁-RLS