A RBF neural network algorithm for blind source separation of linear mixing signals

被引：0

作者：

Lin, Yongman ^{[1
]}

Lin, Tusheng ^{[1
]}

机构：

[1] South China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510640, Peoples R China

来源：

ISDA 2006: SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 3 | 2006年

基金：

中国国家自然科学基金;

关键词：

radial basis function neural network; blind source separation; maximizing entropy (ME) of cost function;

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：

This paper presents a radial basis function (RBF) neural network approach to blind source separation in linear mixture. After calculating center value vector and width value vector, weight value vector that is deduced by maximizing entropy (ME) of cost function is calculated in this RBF neural network. This cost function results in the independence of the outputs with desirable moments such that the original sources are separated properly. Simulation results show that the separation time is reduced and the separation effect is very good. Compared with ME of algorithm, the effect of this algorithm is better.

引用

页码：231 / +

页数：3

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