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Nonlinear Blind Source Separation Using Hybrid Neural Networks.
- Source :
- Advances in Neural Networks - ISNN 2006; 2006, p1165-1170, 6p
- Publication Year :
- 2006
-
Abstract
- This paper proposes a novel algorithm based on minimizing mutual information for a special case of nonlinear blind source separation: post-nonlinear blind source separation. A network composed of a set of radial basis function (RBF) networks, a set of multilayer perceptron and a linear network is used as a demixing system to separate sources in post-nonlinear mixtures. The experimental results show that our proposed method is effective, and they also show that the local character of the RBF network's units allows a significant speedup in the training of the system. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540344391
- Database :
- Supplemental Index
- Journal :
- Advances in Neural Networks - ISNN 2006
- Publication Type :
- Book
- Accession number :
- 32883787
- Full Text :
- https://doi.org/10.1007/11759966_172