Diffusion Bias-Compensated LMS Estimation for Multitask Adaptive Networks

被引：0

作者：

Xu, Xiaoling ^{[1
]}

Jia, Lijuan ^{[1
]}

Xu, Tingting ^{[1
]}

Wan, Hui ^{[1
]}

Shunshoku, Kanae ^{[2
]}

机构：

[1] Beijing Inst Technol, Dept Elect Engn, Beijing 100081, Peoples R China

[2] Fukui Univ Technol, Dept Elect & Elect, Fukui, Japan

来源：

2015 IEEE CONFERENCE ON CONTROL AND APPLICATIONS (CCA 2015) | 2015年

关键词：

ADAPTATION; STRATEGIES; ALGORITHM;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we study the problem of the least mean-square algorithm based on bias compensation in multitask diffusion adaptive networks. Nodes in networks are divided into different clusters and the nodes in the same cluster cooperatively estimate a common parameter. When regressors are corrupted by additive white noise, the estimate results of the traditional multitask diffusion least mean-square (Multi-LMS) algorithm are biased. In order to obtain the unbiased estimation, we propose two multitask diffusion bias-compensated least mean-square (Multi-BCLMS) algorithms by achieving the real-time estimation of the input noise variance, which can be denoted by Multi-BCLMS-A and Multi-BCLMSB respectively. Simulation results show that the two algorithms perform better than the Multi-LMS algorithm in estimation accuracy and mean-square error. Furthermore, the second algorithm (Multi-BCLMS-B) is simpler to implement and the transient is faster than the first one (Multi-BCLMS-A).

引用

页码：545 / 550

页数：6

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