@inproceedings{a67e5490b4174dfe807657db497382b7,
title = "Diffusion bias-compensated least mean square algorithms over multi-agent networks",
abstract = "We study the problem of parameter estimation over multi-agent networks, where all nodes are corrupted by both input and output noise. In that case, traditional least-mean square (LMS) algorithm gives biased results. Fortunately, a bias-compensated LMS (BCLMS) method is proposed with capability to remove the noise-induced bias and get unbiased estimations. However, each node performing bias compensation independently causes an increased variance of local estimates. We propose a diffusion BCLMS algorithm based on the study that the cooperation among all nodes can significantly reduce the local increased estimation variance as well as improve performance of the network. Simulations indicate that the proposed diffusion BCLMS algorithms outperform non-cooperative BCLMS method with lower local mean-square deviation and estimation variance.",
keywords = "Bias-compensated Method, Diffusion Strategy, Increased Estimation Variance, Parameter estimation",
author = "Lu Fan and Lijuan Jia and Tang Tang and Shunshoku Kanae",
note = "Publisher Copyright: {\textcopyright} 2018 Technical Committee on Control Theory, Chinese Association of Automation.; 37th Chinese Control Conference, CCC 2018 ; Conference date: 25-07-2018 Through 27-07-2018",
year = "2018",
month = oct,
day = "5",
doi = "10.23919/ChiCC.2018.8482906",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "4241--4246",
editor = "Xin Chen and Qianchuan Zhao",
booktitle = "Proceedings of the 37th Chinese Control Conference, CCC 2018",
address = "United States",
}