DocumentCode
2910699
Title
Blind separation based on an evolutionary neural network
Author
Chen, Yen-wei ; Zeng, Xiang-Yan ; Nakao, Zensho
Author_Institution
Fac. of Eng., Ryukyus Univ., Okinawa, Japan
Volume
2
fYear
2000
fDate
2000
Firstpage
973
Abstract
We propose an evolutionary neural network for blind source separation. In the proposed method, the separating matrix is used as connection weights of the network, which are updated by a genetic algorithm. A higher-order statistics of kurtosis, which is a simple and original criterion for independence, is used as a fitness function. The applicability of the proposed method for blind source separation is demonstrated by the simulation results
Keywords
genetic algorithms; higher order statistics; neural nets; principal component analysis; signal detection; blind source separation; connection weights; evolutionary neural network; fitness function; genetic algorithm; higher-order statistics; independent component analysis; kurtosis; separating matrix; Blind source separation; Data models; Entropy; Genetic algorithms; Higher order statistics; Independent component analysis; Neural networks; Signal analysis; Signal processing algorithms; Source separation;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location
Barcelona
ISSN
1051-4651
Print_ISBN
0-7695-0750-6
Type
conf
DOI
10.1109/ICPR.2000.906237
Filename
906237
Link To Document