A fast NPCA algorithm for online blind source separation

被引：4

作者：

Zhu, XL ^{[1
]}

Zhang, XD ^{[1
]}

Su, YT ^{[1
]}

机构：

[1] Tsing Hua Univ, Dept Automat, Beijing 100084, Peoples R China

来源：

NEUROCOMPUTING | 2006年 / 69卷 / 7-9期

基金：

中国国家自然科学基金;

关键词：

blind source separation; nonlinear principal component analysis; independent component analysis; step size;

D O I：

10.1016/j.neucom.2005.06.011

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper addresses the problem of blind source separation (BSS) and presents an optimum step size which makes the nonlinear principal component analysis (NPCA) cost function descend in the fastest way. By applying this step size in the self-stabilized NPCA algorithm, a fast NPCA algorithm is obtained. Computer simulations of online BSS show that the new algorithm works more efficiently than the existing least-mean-square (LMS)-type and recursive least-squares (RLS)-type NPCA algorithms. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：964 / 968

页数：5

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