TY - GEN
T1 - Diffusion bias-compensated LMS estimation for multitask adaptive networks
AU - Xu, Xiaoling
AU - Jia, Lijuan
AU - Xu, Tingting
AU - Wan, Hui
AU - Shunshoku, Kanae
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/4
Y1 - 2015/11/4
N2 - 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-BCLMS-B 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).
AB - 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-BCLMS-B 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).
UR - https://www.scopus.com/pages/publications/84964344285
U2 - 10.1109/CCA.2015.7320686
DO - 10.1109/CCA.2015.7320686
M3 - Conference contribution
AN - SCOPUS:84964344285
T3 - 2015 IEEE Conference on Control and Applications, CCA 2015 - Proceedings
SP - 545
EP - 550
BT - 2015 IEEE Conference on Control and Applications, CCA 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Conference on Control and Applications, CCA 2015
Y2 - 21 September 2015 through 23 September 2015
ER -