Blind Source Separation by non-classical (non-quadratic) neural Principal Component Analysis has been investigated by several papers over the recent years, even if particular attention has been paid to the real-valued sources case. The aim of this work is to present an extension of the Kung-Diamantaras' APEX learning rule to non-quadratic complex optimization, and to show the new approach allows blind separation of complex-valued source signals from their linear mixtures.

Neural blind separation of complex sources by extended APEX algorithm (EAPEX) / Fiori, S; Uncini, Aurelio; Piazza, F.. - 5:(1999), pp. 627-630. [10.1109/ISCAS.1999.777650]

Neural blind separation of complex sources by extended APEX algorithm (EAPEX)

UNCINI, Aurelio;
1999

Abstract

Blind Source Separation by non-classical (non-quadratic) neural Principal Component Analysis has been investigated by several papers over the recent years, even if particular attention has been paid to the real-valued sources case. The aim of this work is to present an extension of the Kung-Diamantaras' APEX learning rule to non-quadratic complex optimization, and to show the new approach allows blind separation of complex-valued source signals from their linear mixtures.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/212630
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