Robust Diffusion Adaptive Networks with Noisy Link and Input

Chen Zhu, Lijuan Jia*, Shunshoku Kanae, Zijiang Yang

*此作品的通讯作者

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 study the problem of adaptive parameter estimation for multi-agent distributed networks, where the input regression vectors of network nodes contain Gaussian noises, while the output values and the communication link are polluted by impulse noises. In this case, the estimation performance of traditional diffusion LMS algorithms and most of the state-of-the-art robust distributed algorithms for output impulse noises will degrade significantly. Aiming at this problem, the Minimal Disturbance Bias-Compensated Diffusion Least Mean Square (MDBC-DLMS) algorithm proposed in this paper can effectively suppress noise interference and achieve an acceptable estimation result of the target parameter vector. MDBC-DLMS uses the principle of minimal disturbance to dynamically update the combination coefficients of the diffusion algorithm to effectively suppress the output and link impulse noise. At the same time, it performs dynamic real-time estimation of the input noise variance information to compensate for the estimation bias caused by the input noise. The simulation results show the excellent estimation performance and effectiveness of the method proposed in this paper, and it can accurately estimate the variance information of input noise at the same time.

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
3132-3137
页数6
ISBN(电子版)9789887581536
DOI
出版状态已出版 - 2022
活动41st Chinese Control Conference, CCC 2022 - Hefei, 中国
期限: 25 7月 202227 7月 2022

出版系列

姓名Chinese Control Conference, CCC
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
Hefei
时期25/07/2227/07/22

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