Spline Neural Networks for Blind Separation of Post-Nonlinear-Linear Mixtures
Mirko Solazzi, Aurelio Uncini · IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications · 2004
In this paper, a novel paradigm for blind source separation in the presence of nonlinear mixtures is presented. In particular, the paper addresses the problem of post-nonlinear mixing followed by another instantaneous mixing system. This model is called here the post-nonlinear-linear model. The method is based on the use of the recently introduced flexible activation function whose control points are adaptively changed: a neural model based on adaptive B-spline functions is employed. The signal separation is achieved through an information maximization criterion. Experimental results and comparison with existing solutions confirm the effectiveness of the proposed architecture.