关键词: 盲源分离, 盲提取, 遗传算法, 负熵, 边缘熵, 概率密度估计

Abstract: It offers a fast algorithm for blind extraction based on the improved adaptive genetic algorithm in order to overcome shortcomings in the blind source separation，including slow convergence，not high accuracy and easy to fall into the local optimum.Marginal entropy minimize criterion of independent signal are established on the basis of negentropy criterion.Some of key technologies of the genetic algorithm are improved on the basis of optimization function of blind extraction，so it proposes a fitness function fitting for blind signal extraction and monitoring strategy preventing algorithm from local convergence so that the algorithm can automatically jump out of the best local，and rapidly converges in the global optimum point.Thus blind extraction of transient mixed signals are obtained fast when the improved adaptive genetic algorithm are used as optimization approach.Simulation experiments show that performance of the algorithm is stable，convergence rate high，and global optimum answer achieved，so that blind extraction is realized.

Key words: blind source separation, blind extraction, genetic algorithm, negentropy, marginal entropy, probability density estimation