Application of immune clonal algorithm and mutual information in nonlinear blind source separation

In nonlinear blind source separation (NBSS), usually it is very difficulty to find the global optimal solutions of cost functions due to the existence of many local optimal solutions. In order to overcome the disadvantage mentioned above, a NBSS algorithm based on immune clonal algorithm is proposed in this paper. The odd polynomial function of high-order is used to fit the nonlinear mixed function in this method; the mutual information of separation signals is used as cost function of immune clonal algorithm. In simulation experiment, nonlinear mixed signals are successfully separated by this method. The similar coefficients of separation signals obtained by this algorithm and source signals are higher than 98%. The simulation experiment results have shown that this method can well solve the problem of NBSS.