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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.
Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on (Volume:50 , Issue: 4 )
Date of Publication: Apr 2003