Optimal Variable Momentum Factor Algorithm for NPCA in Blind Source Separation

Abstract

Momentum term technique is an efficient resolution to accelerate the convergence speed of adaptive blind source separation (BSS) algorithms, however, the BSS algorithm combined with a momentum term also suffers from the tradeoff between the fast convergence speed and small misadjustment error. In order to alleviate such compromise, an optimal variable momentum factor method is used to boost the separating performance of the nonlinear principal component analysis (NPCA) BSS algorithm. At first, by using the projection approximation, the cost function of the NPCA algorithm can be represented as a quadratic function of the momentum factor. Then the optimal momentum factor is obtained on the basis of the gradient decent technique, which makes the cost function descend in the fastest way during each iteration. Simulation experiment results manifest that the modified algorithm can improve the convergence speed and decrease the final misadjustment error more effective compared with the classical NPCA algorithms and the fixed momentum factor NPCA algorithms.