Neural blind separation of complex sources by extended Hebbian learning (EGHA)
Simone Fiori, Francesco Piazza · 2003
The aim of this paper is to present a nonlinear extension to Sanger's generalized Hebbian learning rule for complex-valued data neural processing. A possible choice of the involved nonlinearity is discussed recalling the Sudjianto-Hassoun interpretation of the nonlinear Hebbian learning. Extension of this interpretation to the complex case leads to a nonlinearity called Rayleigh function, which allows for separation of mixed independent complex-valued source signals.