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Extended Hebbian learning for blind separation of complex-valued sources

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1 Author(s)
Fiori, S. ; Neural Networks & Circuit Theor. Res. Group, Perugia Univ., Terni, Italy

The aim of this work is to present a nonlinear extension to Sanger's generalized Hebbian learning algorithm for complex-valued signal processing by neural networks. A possible choice of the involved nonlinearity is discussed by recalling the Sudjianto-Hassoun interpretation of nonlinear Hebbian learning. An extension of this interpretation to the complex-valued case leads to a Rayleigh nonlinearity, that allows for separating mixed independent complex-valued circular source signals.

Published in:

Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on  (Volume:50 ,  Issue: 4 )

Date of Publication:

Apr 2003

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