.png){kind=icon}
返回
A normalized robust mixed-norm adaptive algorithm for system identification
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2003.819353.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A normalized robust mixed-norm (NRMN) algorithm for system identification in the presence of impulsive noise is introduced. The standard robust mixed-norm (RMN) algorithm exhibits slow convergence, requires a stationary operating environment, and employs a constant step-size that needs to be determined a priori. To overcome. these limitations the proposed NRMN algorithm introduces a time-varying learning rate and, thus, no longer requires a stationary environment, a major drawback of the RMN algorithm. The proposed NRMN exhibits increased convergence rate and substantially reduces the steady-state coefficient error, as compared to the least mean square (LMS), normalized LMS (NLMS), least absoluted deviation (LAD), and RMN algorithm.
Keyword:
adaptive filtering
impulsive noise
mixed-norm adaptive filtering
normalized step-size
system identification
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:
9.6
论文数:
1.1W
被引数:
1.7W
机构
暂无机构信息
.png){kind=icon}
