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基于峭度的独立分量算法的性能分析研究
引用本文:钟丽莉,熊兴中.基于峭度的独立分量算法的性能分析研究[J].四川轻化工学院学报,2014(4):43-47.
作者姓名:钟丽莉  熊兴中
作者单位:四川理工学院自动化与电子信息学院,四川自贡643000
基金项目:四川省杰出青年基金项目(2011JQ0034);四川省省属高校科研创新团队建设计划基金项目(13TD0017);人工智能四川省重点实验室基金项目(2012RYJ05)
摘    要:独立分量算法是一种应用非常广泛的盲信号处理算法.而峭度作为一种重要的信号分析工具,可以有效地进行优化分析.然而,对于各种不同类型的算法的对比分析目前还少有介绍,所以有必要对基于峭度的FastICA和RobustICA两种独立分量算法进行对比分析研究.理论分析及实验结果表明,鲁棒独立分量法RobustICA在鲁棒性、收敛性和复杂度方面整体优于快速定点独立分量法FastICA,从而为实际应用提供一定的参考价值.

关 键 词:峭度  快速定点独立分量法  鲁棒独立分量法  鲁棒性  收敛  复杂度

Research on Performance Analysis of Independent Component Algorithm Based on Kurtosis
ZHONG Lili,XIONG Xingzhong.Research on Performance Analysis of Independent Component Algorithm Based on Kurtosis[J].Journal of Sichuan Institute of Light Industry and Chemical Technology,2014(4):43-47.
Authors:ZHONG Lili  XIONG Xingzhong
Affiliation:( School of Automation and Electronic Information, Sichuan University of Science & Engineering, Zigong 643000, China)
Abstract:Independent component analysis algorithm is a widely used algorithm for blind signal processing. As an important signal analysis tool, kurtosis can be effective for optimization and analysis. However, there is little introduction to the comparison and analysis for various types of algorithms currently. Therefore, it is necessary to research and analyze the comparison between FastlCA and RobustlCA based on kurtosis. The theoretical analysis and the simulation results indicate that the robustness, convergence and complexity of RobustlCA are better than that of FastlCA on the whole. Thus it provides the reference for practical applications.
Keywords:kurtosis  FastlCA  RobustlCA  robustness  convergence  complexity
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