On blind separation of complex-valued sources by extended Hebbian learning

S. Fiori
2001 IEEE Signal Processing Letters  
The aim of this letter is to present a nonlinear extension to Sanger's generalized Hebbian learning algorithm for complex-valued data neural processing, which allows for separating mixed independent circular source signals. The proposed generalization relies on an interesting interpretation of nonclassical Hebbian learning proposed by Sudjianto and Hassoun for real-valued neural units. Index Terms-Blind source separation, neural networks, Hebbian learning.
doi:10.1109/97.935735 fatcat:x2auyjxnufblbbcrbohd5t472q