Bias-compensated Sparse RLS Algorithms Over Distributed Networks

Senran Peng; Lijuan Jia; Shunshoku Kanae; Zi Jiang Yang

doi:10.23919/CCC55666.2022.9901566

Bias-compensated Sparse RLS Algorithms Over Distributed Networks

Senran Peng, Lijuan Jia^*, Shunshoku Kanae, Zi Jiang Yang

^*此作品的通讯作者

信息与电子学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

In this paper, we propose a bias-compensated method based on the L1-RLS algorithm and the diffusion L1-RLS algorithm for sparse system identification. Our proposed algorithms improve the estimation accuracy of traditional L1-RLS when the input data is corrupted by input noises. Furthermore, we give simulation results to verify that proposed algorithms have better estimation accuracy than other sparse RLS algorithms without bias compensation, it also proves that results are unbiased under input noises.

源语言	英语
主期刊名	Proceedings of the 41st Chinese Control Conference, CCC 2022
编辑	Zhijun Li, Jian Sun
出版商	IEEE Computer Society
页	3138-3143
页数	6
ISBN（电子版）	9789887581536
DOI	https://doi.org/10.23919/CCC55666.2022.9901566
出版状态	已出版 - 2022
活动	41st Chinese Control Conference, CCC 2022 - Hefei, 中国期限: 25 7月 2022 → 27 7月 2022

出版系列

姓名	Chinese Control Conference, CCC
卷	2022-July
ISSN（印刷版）	1934-1768
ISSN（电子版）	2161-2927

会议

会议	41st Chinese Control Conference, CCC 2022
国家/地区	中国
市	Hefei
时期	25/07/22 → 27/07/22

访问文件

10.23919/CCC55666.2022.9901566

其它文件与链接

链接到 Scopus 的出版物

引用此

Peng, S., Jia, L., Kanae, S., & Yang, Z. J. (2022). Bias-compensated Sparse RLS Algorithms Over Distributed Networks. 在 Z. Li, & J. Sun (编辑), Proceedings of the 41st Chinese Control Conference, CCC 2022 (页码 3138-3143). (Chinese Control Conference, CCC; 卷 2022-July). IEEE Computer Society. https://doi.org/10.23919/CCC55666.2022.9901566

@inproceedings{6770a1f198d44302895ee208c962a8d1,

title = "Bias-compensated Sparse RLS Algorithms Over Distributed Networks",

abstract = "In this paper, we propose a bias-compensated method based on the L1-RLS algorithm and the diffusion L1-RLS algorithm for sparse system identification. Our proposed algorithms improve the estimation accuracy of traditional L1-RLS when the input data is corrupted by input noises. Furthermore, we give simulation results to verify that proposed algorithms have better estimation accuracy than other sparse RLS algorithms without bias compensation, it also proves that results are unbiased under input noises.",

keywords = "Bias-compensation, diffusion networks, distributed networks, recursive least squares, sparse system identification",

author = "Senran Peng and Lijuan Jia and Shunshoku Kanae and Yang, {Zi Jiang}",

note = "Publisher Copyright: {\textcopyright} 2022 Technical Committee on Control Theory, Chinese Association of Automation.; 41st Chinese Control Conference, CCC 2022 ; Conference date: 25-07-2022 Through 27-07-2022",

year = "2022",

doi = "10.23919/CCC55666.2022.9901566",

language = "English",

series = "Chinese Control Conference, CCC",

publisher = "IEEE Computer Society",

pages = "3138--3143",

editor = "Zhijun Li and Jian Sun",

booktitle = "Proceedings of the 41st Chinese Control Conference, CCC 2022",

address = "United States",

}

Peng, S, Jia, L, Kanae, S & Yang, ZJ 2022, Bias-compensated Sparse RLS Algorithms Over Distributed Networks. 在 Z Li & J Sun (编辑), Proceedings of the 41st Chinese Control Conference, CCC 2022. Chinese Control Conference, CCC, 卷 2022-July, IEEE Computer Society, 页码 3138-3143, 41st Chinese Control Conference, CCC 2022, Hefei, 中国, 25/07/22. https://doi.org/10.23919/CCC55666.2022.9901566

Bias-compensated Sparse RLS Algorithms Over Distributed Networks. / Peng, Senran; Jia, Lijuan; Kanae, Shunshoku 等.
Proceedings of the 41st Chinese Control Conference, CCC 2022. 编辑 / Zhijun Li; Jian Sun. IEEE Computer Society, 2022. 页码 3138-3143 (Chinese Control Conference, CCC; 卷 2022-July).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Bias-compensated Sparse RLS Algorithms Over Distributed Networks

AU - Peng, Senran

AU - Jia, Lijuan

AU - Kanae, Shunshoku

AU - Yang, Zi Jiang

PY - 2022

Y1 - 2022

N2 - In this paper, we propose a bias-compensated method based on the L1-RLS algorithm and the diffusion L1-RLS algorithm for sparse system identification. Our proposed algorithms improve the estimation accuracy of traditional L1-RLS when the input data is corrupted by input noises. Furthermore, we give simulation results to verify that proposed algorithms have better estimation accuracy than other sparse RLS algorithms without bias compensation, it also proves that results are unbiased under input noises.

AB - In this paper, we propose a bias-compensated method based on the L1-RLS algorithm and the diffusion L1-RLS algorithm for sparse system identification. Our proposed algorithms improve the estimation accuracy of traditional L1-RLS when the input data is corrupted by input noises. Furthermore, we give simulation results to verify that proposed algorithms have better estimation accuracy than other sparse RLS algorithms without bias compensation, it also proves that results are unbiased under input noises.

KW - Bias-compensation

KW - diffusion networks

KW - distributed networks

KW - recursive least squares

KW - sparse system identification

UR - http://www.scopus.com/inward/record.url?scp=85140459630&partnerID=8YFLogxK

U2 - 10.23919/CCC55666.2022.9901566

DO - 10.23919/CCC55666.2022.9901566

M3 - Conference contribution

AN - SCOPUS:85140459630

T3 - Chinese Control Conference, CCC

SP - 3138

EP - 3143

BT - Proceedings of the 41st Chinese Control Conference, CCC 2022

A2 - Li, Zhijun

A2 - Sun, Jian

PB - IEEE Computer Society

T2 - 41st Chinese Control Conference, CCC 2022

Y2 - 25 July 2022 through 27 July 2022

ER -

Bias-compensated Sparse RLS Algorithms Over Distributed Networks

摘要

出版系列

会议

访问文件

其它文件与链接

指纹

引用此