| 基于粒子群优化的独立分量分析算法研究 |
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| 引用本文： | 张文希,郑茂. 基于粒子群优化的独立分量分析算法研究[J]. 科学技术与工程, 2010, 10(8) |
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| 作者姓名： | 张文希 郑茂 |
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| 作者单位： | 1. 长沙学院电子与通信工程系,长沙,410003
2. 国防科学技术大学电子科学与工程学院,长沙,410073 |
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| 摘 要： | 在分析独立分量分析算法的基础上,给出了一种基于粒子群优化的独立分量分析算法。该算法以互信息量最小化为目标函数,通过对粒子群位置矢量和速度矢量更新的改进,得到全局最优值,从而得到分离矩阵。仿真实验表明,基于粒子群优化的独立分量分析算法是一种非常有效的盲源分离算法。
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| 关 键 词： | 独立分量分析 互信息 粒子群优化 适应度函数 |
| 收稿时间： | 2009-10-28 |
| 修稿时间： | 2009-12-29 |
Research on Independent Component Analysis Based on Particle Swarm Optimization Algorithms |
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| Zhang Wenxi and Zheng Mao. Research on Independent Component Analysis Based on Particle Swarm Optimization Algorithms[J]. Science Technology and Engineering, 2010, 10(8) |
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| Authors: | Zhang Wenxi and Zheng Mao |
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| Affiliation: | Department of Electronic and Communication Engineering/a>;Changsha University/a>;Changsha 410003/a>;P.R.China/a>;School of Electronic Science and Engineering/a>;National University of Defense Technology1/a>;Changsha 410073/a>;P.R.China |
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| Abstract: | On the basis of analyzing the independent component analysis algorithms, a novel method based on particle swarm optimization was proposed to minimize the mutual information, which through improving position vector and velocity vector to get the global optimization solution and then separate the mixed signals.The simulation results showed that the independent component analysis based on particle swarm optimization was a more efficient algorithm. |
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| Keywords: | independent component analysis mutual information particle swarm optimization fitness function |
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