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Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 3
A RBF Neural Network Algorithm for Blind Source Separation of Linear Mixing Signals
Jinan, China
October 16-October 18
ISBN: 0-7695-2528-8
ASCII Text
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Yongman Lin, Tusheng Lin, "A RBF Neural Network Algorithm for Blind Source Separation of Linear Mixing Signals," Intelligent Systems Design and Applications, International Conference on, vol. 3, pp. 231-235, Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 3, 2006.
 
 
BibTex
x
 
@article{ 10.1109/ISDA.2006.5,
author = {Yongman Lin and Tusheng Lin},
title = {A RBF Neural Network Algorithm for Blind Source Separation of Linear Mixing Signals},
journal ={Intelligent Systems Design and Applications, International Conference on},
volume = {3},
year = {2006},
isbn = {0-7695-2528-8},
pages = {231-235},
doi = {http://doi.ieeecomputersociety.org/10.1109/ISDA.2006.5},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Intelligent Systems Design and Applications, International Conference on
TI - A RBF Neural Network Algorithm for Blind Source Separation of Linear Mixing Signals
SN - 0-7695-2528-8
SP231
EP235
A1 - Yongman Lin,
A1 - Tusheng Lin,
PY - 2006
KW - radial basis function neural network. blind source separation. maximizing entropy (ME) of cost function.
VL - 3
JA - Intelligent Systems Design and Applications, International Conference on
ER -
 
Yongman Lin, South China University of Technology, China
Tusheng Lin, South China University of Technology, China
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.
Index Terms:
radial basis function neural network. blind source separation. maximizing entropy (ME) of cost function.
Citation:
Yongman Lin, Tusheng Lin, "A RBF Neural Network Algorithm for Blind Source Separation of Linear Mixing Signals," isda, vol. 3, pp.231-235, Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 3, 2006
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