Nonlinear blind source separation combining with improved particle swarm optimization

The traditional nonlinear blind source separation (NBSS) algorithms often fall across the problem of local optimal solution to lead a lower separation precision. An NBSS algorithm based on improved particle swarm optimization (PSO) is proposed, where the multilayer perception (MLP) is used to fit the inverse of the nonlinear mixed process, and the mutual information between separated signals is regarded as the optimization objective (Fitness function of PSO) to realize the optimization of parameters in MLP. However, the canonical PSO algorithms usually suffer from particle premature problems and are easy to get into local optimal solution. Thus crossover and mutation operations are applied to the particles with lower fitness according to probability mechanism to efficiently increase the diversity of the particles, and the premature problem of canonical PSO is solved. The simulations and experiments show that compared with the linear blind source separation algorithm and the NBSS algorithm based on canonical PSO, the proposed algorithm enables to extract pure independent source information from mechanical information with nonlinear mixing and improve the separation precision of nonlinear mixed signals.