Improved Particle Swarm to Nonlinear Blind Source Separation

This paper propose the nonlinear blind source separation algorithm using improved particle swarm optimization (PSO) combined with natural gradient algorithm. The model of nonlinear blind source separation (NBSS) is built which the nonlinear transfer function is simulate by the P-th order polynomial function. At the same time, Based on the character of particle swarm optimization (PSO), "migrating operator" and local area deep-searching is introduce into PSO. Then, the parameters of the P-th order polynomial function is estimated by PSO. Experimental results indicate that tire established algorithm of PSO can quickly and effectively get optimal resolution to the nonlinear blind source separation.