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POLARIZED SIGNAL CLASSIFICATION BY COMPLEX AND QUATERNIONIC MULTI-LAYER PERCEPTRONS.

Authors :
BUCHHOLZ, SVEN
LE BIHAN, NICOLAS
Source :
International Journal of Neural Systems; Apr2008, Vol. 18 Issue 2, p75-85, 11p, 5 Charts, 3 Graphs
Publication Year :
2008

Abstract

For polarized signals, which arise in many application fields, a statistical framework in terms of quaternionic random processes is proposed. Based on it, the ability of real-, complex- and quaternionic-valued multi-layer perceptrons (MLPs) of performing classification tasks for such signals is evaluated. For the multi-dimensional neural networks the relevance of class label representations is discussed. For signal to noise separation it is shown that the quaternionic MLP yields an optimal solution. Results on the classification of two different polarized signals are also reported. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01290657
Volume :
18
Issue :
2
Database :
Complementary Index
Journal :
International Journal of Neural Systems
Publication Type :
Academic Journal
Accession number :
31772013
Full Text :
https://doi.org/10.1142/S0129065708001403