基于量子粒子群优化算法的信号盲源分离

Blind source separation based on quantum particle swarm optimization algorithm

为了获得更优的盲源分离效果,针对标准粒子群算法难以求解独立分量分析算法目标函数的难题,提出了一种基于量子粒子群算法的盲源分离方法。首先建立混合信号非线性盲源分离的数学模型,然后将量子粒子群算法引入到独立分量分析算法目标函数求解过程中,以确定最优分离矩阵,最后对其性能进行验证性实验。实验结果表明,本文方法加快了混合信号盲源分离速度,获得了更加理想的混合信号盲源分离效果。

In order to obtain a better blind source separation effect,the standard particle swarm algorithm is difficulty to solve target function of independent component analysis algorithm,so s paper put forward a method of blind source separation based on quantum particle swarm algorithm. After the establishment of mathematical model of nonlinear blind source separation of mixed signals,and then the quantum particle swarm algorithm is introduced to solve the objective function of independent component analysis algorithm to determine the optimal separation matrix,finally the performance is tested by experiments. The experimental results show that the proposed method speeds up separation speed of blind source,and obtains more ideal blind source separation effect.