Blind source separation of nonlinear mixing models
Te-Won Lee, Bert-Uwe Koehler, Reinhold Orglmeister · 2002
We present a new set of learning rules for the nonlinear blind source separation problem based on the information maximization criterion. The mixing model is divided into a linear mixing part and a nonlinear transfer channel. The proposed model focuses on a parametric sigmoidal nonlinearity and higher order polynomials. Our simulation results verify the convergence of the proposed algorithms.