@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",
}