On Initial Convergence Behavior of the Kernel Least Mean Square Algorithm

被引：0

作者：

Chen, Badong ^{[1
]}

Wang, Ren ^{[1
]}

Zheng, Nanning ^{[1
]}

Principe, Jose C. ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China

来源：

2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2015年

关键词：

KLMS; mean square convergence; weight error power (WEP); excess mean square error (EMSE);

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The mean square convergence of the kernel least mean square (KLMS) algorithm has been studied in a recent paper [B. Chen, S. Zhao, P. Zhu, J. C. Principe, Mean square convergence analysis of the kernel least mean square algorithm, Signal Processing, vol. 92, pp. 2624-2632, 2012]. In this paper, we continue this study and focus mainly on the initial convergence behavior. Two measures of the convergence performance are considered, namely the weight error power (WEP) and excess mean square error (EMSE). The analytical expressions of the initial decreases of the WEP and EMSE are derived, and several interesting facts about the initial convergence are presented. An illustration example is given to support our observation.

引用

页数：5

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