Adaptive Improved Natural Gradient Algorithm for Blind Source Separation
We propose an adaptive improved natural gradient algorithm for blind separation of independent sources. First, inspired by the well known backpropagation algorithm, we incorporate a momentum term into the natural gradient learning process to accelerate the convergence rate and improve the stability. Then an estimation function for the adaptation of the separation model is obtained to adaptively control a step size parameter and a momentum factor. The proposed natural gradient algorithm with variable step size parameter and variable momentum factor is therefore particularly well suited to blind source separation in a time varying environment, such as an abruptly changing mixing matrix or signal power. The expected improvement in the convergence speed, stability, and tracking ability of the proposed algorithm is demonstrated by extensive simulation results in both time invariant and time varying environments. The ability of the proposed algorithm to separate extremely weak or badly scaled sources is also verified. In addition, simulation results show that the proposed algorithm is suitable for separating mixtures of many sources (e.g. the number of sources is 10) in the complete case.
Neural Computation