摘要
针对现有的盲源分离算法性能大多依赖于非线性函数的选取问题,提出了一种基于自适应粒子群优化(adaptive particle swarm optimization,APSO)的盲源分离算法。该算法以分离信号的负熵为目标函数,根据分离信号的状态自适应地调整惯性因子,克服了收敛速度和信号恢复质量之间的矛盾。仿真实验表明,该算法的性能对源信号的概率密度性质没有依赖性,因而能很好地分离亚高斯和超高斯信号的混合信号,并且能有效地避免早熟收敛问题,具有较快的收敛速度,分离效果好。
The performance of existing blind source function that is selected according to the distribution of separation methods is affected by the non-linear contrast original signals. To solve this problem, a blind source separation algorithm based on adaptive particle swarm optimization is proposed, which takes the negentropy of mixtures as a contrast function. The inertia weight factor depends on the negentropy, which can improve the contradiction between the convergence speed and the performance of separated signals. The simulated results show that the proposed method could separate the mixture of both super-Gaussian signal and sub-Gaussian signal, and the proposed algorithm could efficiently alleviate the problem of premature convergence and has a faster convergence speed than PSO.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2009年第6期1275-1278,共4页
Systems Engineering and Electronics
基金
国家自然科学基金项目资助课题(60672034)
关键词
粒子群算法
盲源分离
自适应
惯性因子
particle swarm optimization
blind source separation
adaptive
inertia weight factor
