Complex Independent Component Analysis by Nonlinear Generalized Hebbian Learning with Rayleigh Nonlinearity

The aim of this paper is to present a non-linear exten-sion of the Sanger’s Generalized Hebbian Algorithm to the processing of complex-valued data. A possible choice of the involved non-linearity is discussed recall-ing the Sudjianto-Hassoun interpretation of the non-linear Hebbian learning. Extension of this interpreta-tion to the complex case leads to a nonlinearity called Rayleigh function, which allows for separating mixed independent complex-valued source signals. 1