BLIND SOURCE SEPARATION IN NONLINEAR MIXTURES BY ADAPTIVE SPLINE NEURAL NETWORKS
Mirko Solazzi, Raffaele Parisi, Aurelio Uncini · 2001
In this paper a novel paradigm for blind source separation in the presence of nonlinear mixtures is presented and described. The proposed approach employs a neural model based on adaptive B-spline functions. Signal separation is achieved through an information maximization criterion. Experimental results and comparison with existing solutions confirm the effectiveness of the proposed architecture.