Nonlinear blind separation using an RBF network model

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