A Diversity-guided Particle Swarm Optimization Method for Blind Source Separation

被引：0

作者：

Wei, Shuang ^{[1
]}

Jiang, Defu ^{[1
]}

Gao, Yang ^{[1
]}

机构：

[1] Hohai Univ, Coll Comp & Informat Hohai, Array & Informat Proc Lab, Nanjing 210098, Jiangsu, Peoples R China

来源：

APPLIED SCIENCE, MATERIALS SCIENCE AND INFORMATION TECHNOLOGIES IN INDUSTRY | 2014年 / 513-517卷

关键词：

Particle swarm optimization; diversity-guided; blind source separation; independent component analysis;

D O I：

10.4028/www.scientific.net/AMM.513-517.876

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

This paper presents a diversity-guided Particle swarm optimization (PSO) algorithm to resolve the Blind source separation (BSS) problem. Because the independent component analysis (ICA) approach, a popular method for the BSS problem, has a shortcoming of premature convergence during the optimization process, the proposed PSO algorithm aims to improve this issue by using the diversity calculation to avoid trapping in the local optima. In the experiment, the performance of the proposed PSO algorithm for the BSS problem has been investigated and the results are compared with the conventional PSO algorithm. It shows that the proposed PSO algorithm outperforms the conventional PSO algorithm.

引用

下载

页码：876 / 880

页数：5

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