Nonlinear blind separation using an RBF network model

A novel neural network approach is developed for nonlinear blind separation using a radial b axis function (RBF) network and an information theoretic criterion. By utilizing the universal approximation ability and local response property of an RBF network the proposed separation method is characterized by fast convergence and strong demixing ability. After its learning process, the RBF network is able to separate independent signals effectively from their nonlinear mixtures by the nonlinear channel model without the prior knowledge of the source signals and mixing channels. Experimental results illustrate the validity and effectiveness of the proposed method