孔祥翠;王微微;陈静静;陈宇;

• 1:中国石油大学(华东)信息与控制工程学院

摘要(Abstract)：

针对传统独立分量分析(ICA)算法在含噪情况下分离效果不好,容易陷入局部收敛的问题,提出基于入侵性杂草优化(IWO)算法的有噪独立分量分析方法.以分离信号负熵和为目标函数,选用高斯密度函数估计负熵,消除目标函数中的不稳定项,提高算法的稳定性和准确性;采用入侵性杂草优化算法估计混合矩阵,提高算法的全局寻优性能.仿真结果表明:与传统Fast ICA和Fast NoisyICA算法相比,文中算法的分离信号和源信号的相似因数更大,随着信噪比增加,相似因数趋向1,可以更好地估计源信号;PI指标明显小于其他两种算法的,可以更为精确地估计混合矩阵.研究结果对有噪ICA信号处理有一定参考意义.

关键词(KeyWords)：入侵性杂草优化算法;有噪独立分量分析;高斯密度函数;混合矩阵

Abstract:

Keywords:

基金项目(Foundation):国家自然科学基金项目(51304231);; 山东省自然科学基金项目(ZR2010EQ015)

作者(Author):孔祥翠;王微微;陈静静;陈宇;

Email:

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