基于改进人工蜂群算法的盲源分离算法

针对传统盲源分离算法收敛速度与分离性能间的矛盾,提出一种基于改进人工蜂群算法的盲源分离算法.该算法利用信号的峰度绝对值作为被优化目标函数,对人工蜂群算法中跟随蜂阶段的搜索过程进行改进,使人工蜂群算法在初始阶段可以快速收敛到最优解所在区域,具有更高的收敛精度.使用改进后的人工蜂群算法对传统盲源分离算法中的初始分离矩阵进行优化,再利用优化的初始分离矩阵进行信号分离.仿真结果表明,改进后的算法能够显著加快收敛速度并保持较好的分离性能值,较好地解决了收敛速度与分离性能间的矛盾.

Blind source separation based on an improved artificial bee colony algorithm

In view of the contradiction between convergence speed and separation performance of blind source separation algorithm , a new separation algorithm based on an improved artificial bee colony algorithm was proposed . This proposed algorithm used the absolute value of mixing signal kurtosis as the optimization objective function ,the search process of the onlooker stage in the artificial bee colony algorithm had been improved . Then the improved artificial bee colony algorithm could converge quickly at the initial stage and improved the global convergence performance at the last stage ,the mixing signals could be separated after using the improved artificial bee colony algorithm to optimize the initial separation matrix .Simulation results showed that the improved algorithm could significantly speed up the convergence rate and maintain a good convergence performance ,and that it could solve the contradiction betw een convergence speed and separation performance of blind source separation algorithm .