An approach for nonlinear blind source separation of signals with noise using neural networks and higher-order cumulants

We propose a robust approach for blind source separation when observations are contaminated with Gaussian noise and nonlinear distortion. A radial basis function network (RBFN) is employed to estimate the inverse of the nonlinear mixing matrix. We utilize an novel cost function which consists of mutual information and higher-order cumulants of signals. Compared with moments, higher-order cumulants can provide a clearer form and more information of signals. Thus, the proposed method has not only the capacity of recovering the nonlinearly mixed signals, but also removing high-level Gaussian noise from transmitted signals. Through simulation and analysis of artificially synthesized signals, we illustrate the efficacy of this approach.