A fast NPCA algorithm for online blind source separation

This paper addresses the problem of blind source separation (BSS) and presents an optimum step size which makes the nonlinear principal component analysis (NPCA) cost function descend in the fastest way. By applying this step size in the self-stabilized NPCA algorithm, a fast NPCA algorithm is obtained. Computer simulations of online BSS show that the new algorithm works more efficiently than the existing least-mean-square (LMS)-type and recursive least-squares (RLS)-type NPCA algorithms.