Kernel Recursive Generalized Maximum Correntropy

被引：57

作者：

Zhao, Ji ^{[1
]}

Zhang, Hongbin ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Elect Engn, Chengdu 611731, Sichuan, Peoples R China

来源：

IEEE SIGNAL PROCESSING LETTERS | 2017年 / 24卷 / 12期

基金：

美国国家科学基金会; 中国博士后科学基金; 中国国家自然科学基金;

关键词：

Generalized maximum correntropy (GMC) criterion; kernel adaptive filter (KAF); non-Gaussian noise; recursive;

D O I：

10.1109/LSP.2017.2761886

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this letter, a novel kernel adaptive algorithm, called kernel recursive generalized maximum correntropy algorithm (KRGMC), is derived in a kernel space and under the generalized maximum correntropy (GMC) criterion. The proposed kernel algorithm can effectively scale down the dynamic recursive weight coefficients influenced by the impulsive estimate error to avoid the significant performance degradation. The superior performance of the proposed algorithm is verified by numerical simulations about short-time series prediction in alpha-stable noise environment.

引用

页码：1832 / 1836

页数：5

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