In this paper the source recovery of nonlinear mixtures in the complex domain is addressed by an Independent Component Analysis (ICA) approach. Extending the wellknown real PNL mixtures, source recovery is performed by a complex INFOMAX approach. Nonlinear complex functions involved in the learning process are realized by pairs of spline neurons called “splitting functions”, working on the real and the imaginary part of the signal respectively. A simple adaptation algorithm is derived and some experimental results that demonstrate the effectiveness of the proposed method are shown.
A flexible Blind Source Recovery in Complex Nonlinear Environment / D., Vigliano; Scarpiniti, Michele; Parisi, Raffaele; Uncini, Aurelio. - (2006), pp. 3059-3063. (Intervento presentato al convegno International Symposium on Intelligent Control tenutosi a Monaco, Germania nel 04-06/10/2006) [10.1109/ISIC.2006.285558].
A flexible Blind Source Recovery in Complex Nonlinear Environment
SCARPINITI, MICHELE;PARISI, Raffaele;UNCINI, Aurelio
2006
Abstract
In this paper the source recovery of nonlinear mixtures in the complex domain is addressed by an Independent Component Analysis (ICA) approach. Extending the wellknown real PNL mixtures, source recovery is performed by a complex INFOMAX approach. Nonlinear complex functions involved in the learning process are realized by pairs of spline neurons called “splitting functions”, working on the real and the imaginary part of the signal respectively. A simple adaptation algorithm is derived and some experimental results that demonstrate the effectiveness of the proposed method are shown.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
