.png){kind=icon}
返回
A variable forgetting factor diffusion recursive least squares algorithm for distributed estimation
.png){kind=icon}
OA
AI
DOI：10.1016/j.sigpro.2017.05.010.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Distributed recursive least squares (RLS) algorithms have superior convergence properties compared to the least mean squares (LMS) counterpart. However, with a fixed forgetting factor (FF), they are not suitable for tracking time-varying (TV) parameters. This paper proposes a novel diffusion variable FF RLS (Diff-VFF-RLS) algorithm based on a local polynomial modeling (LPM) of the unknown TV system. The diffusion RLS solution is derived analytically such that the estimation deviation from the true value is investigated. Based on the analysis and the LPM of the TV system, a new optimal VFF formula that tries to minimize the estimation deviation is obtained. Simulations are conducted to verify the theoretical analysis in terms of the steady-state mean square deviation (MSD) and the VFF formula. Results also show that the convergence and tracking performance of the proposed algorithm compares favorably with conventional ones. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Adaptive networks
Diffusion RLS
MSD analysis
VFF
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
机构
引用论文
A comparative study of three recursive least-squares algorithms for single-tone frequency tracking三种单音频率跟踪递推最小二乘算法的比较研究.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏Diffusion sign-error LMS algorithm: Formulation and stochastic behavior analysisSIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏分享收藏
查看更多内容