.png){kind=icon}
返回
Kernel Least Mean p-Power Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2017.2702714.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This letter proposes a novel kernel least mean p-power (KLMP) algorithm for nonlinear system identification in the presence of additive non-Gaussian impulsive noises, modeled by a symmetric alpha-stable distribution with heavy tail. The KLMP algorithm based on the fractional lower order statistics error criterion can effectively scale down the dynamic recursive weight coefficients affected by the impulsive estimation error to avoid the significant performance degradation. Simulation results demonstrate that the proposed algorithm has favorable convergence properties than the classical kernel least-mean-square algorithm using a conventional error criterion in the non-Gaussian impulsive environment.
Keyword:
Fractional lower order statistics (FLOS)
kernel least mean p-power (KLMP) algorithm
symmetric alpha-stable (S alpha S) distribution
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
引用论文
DATA‐INFORMED SAFETY ASSESSMENT OF EXISTING TRANSPORT INFRASTRUCTURES基于数据的现有交通基础设施安全评估.png)
ce/papers
OAAI
ce/papersIF0
OAAI分享收藏
查看更多内容