Title :
A neural network for the blind separation of non-Gaussian sources
Author :
Freisleben, Bernd ; Hagen, Claudia ; Borschbach, Markus
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Siegen Univ., Germany
Abstract :
In this paper, a two-layer neural network is presented, which organizes itself to perform blind source separation. The inputs to the network are prewhitened linear mixtures of unknown independent source signals. An unsupervised nonlinear Hebbian learning rule is used for training the network. After convergence, the network is able to extract the source signals from the mixtures, provided that the source signals do not have Gaussian distributions
Keywords :
Hebbian learning; convergence; feedforward neural nets; signal detection; unsupervised learning; blind source separation; convergence; independent component analysis; multilayer neural network; nonGaussian sources; nonlinear Hebbian learning; prewhitened linear mixtures; signal extraction; unsupervised learning; Array signal processing; Blind source separation; Computer science; Convergence; Independent component analysis; Neural networks; Principal component analysis; Proposals; Source separation; Unsupervised learning;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.685876
