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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
