期刊文献+

基于相空间同步的多变量序列相关性分析及预测 被引量:4

Correlation analysis and prediction of multivariate time series based on phase space synchronization
在线阅读 下载PDF
导出
摘要 针对多变量混沌序列相关性分析中各分量幅值之间可能没有明显的相关性,但在其相空间邻域内会产生同步特性的问题,提出一种从相空间同步角度研究两个变量间相互依赖关系的非线性相关分析方法。首先按照对应的时间标记将原始变量相空间中的邻域点向另外一个变量中进行投影,分析映射前后邻域半径的变化,在此基础上定义一种度量变量间非线性相关性的评价指标。最后构建多变量局域预测模型,实现对多变量混沌序列的精确预测。仿真实例验证了结果的有效性。 Considering the characteristic of chaotic time series that may produce synchronization in the phase space although there is no obvious correlation between the amplitudes of various components,a nonlinear correlation analysis algorithm based on phase space synchronization is presented.Firstly,the nearest neighborhood of one time series is mapping into the phase space of another time series according to the time subscript,and the change of the neighborhood radius is calculated.Then,a criterion is defined to measure the nonlinear dependence between two time series.Finally,a local prediction model is constructed to implement the precise prediction of multivariate time series.Simulation results show the effectiveness of the designed method.
作者 韩敏 魏茹
出处 《系统工程与电子技术》 EI CSCD 北大核心 2010年第11期2426-2430,共5页 Systems Engineering and Electronics
基金 国家自然科学基金(60674073) 国家重点基础研究发展计划(973计划)项目(2006CB403405) 国家科技支撑计划项目(2006BAB14B05)资助课题
关键词 多变量混沌序列 邻域 相空间同步 相关分析 局域预测 multivariate chaotic time series nearest neighborhood phase space synchronization correlation analysis local prediction
  • 相关文献

参考文献18

  • 1William W H.Nonlinear multivariate and time series analysis by neural network methods[J].Review of Geophysics,2004,42:1-25.
  • 2席剑辉,魏茹,韩敏.EKF在多变量混沌序列辨识中的应用[J].系统仿真学报,2006,18(9):2525-2529. 被引量:4
  • 3Wichard J D,Merkwirth C,Ogorzalek M.Detecting correlation in stock market[J].Physica A:Statistical Mechanics and its Applications,2004,344(1-2):308-311.
  • 4Lai P L,Fyfe C.Kernel and nonlinear canonical correlation analysis[J].International Journal of Neural Systems,2000,10(5):365-377.
  • 5Peng H C,Long F H,Ding C.Feature selection based on mutual information:criteria of max-dependency,max-relevance,and minredundancy[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,2005,27(8):1226-1238.
  • 6Rulkov N F,Sushchik M M,Tsimring L S,et al.Generalized synchronization of chaos in directionally coupled chaotic systems[J].Physica Review E,1995,51(2):980-994.
  • 7Le van Quyen M,Martinerie J,Adam C,et al.Nonlinear analyses of interictal EEG map the brain interdependences in human focal epilepsy[J].Physica D:Nowlinear Phenomena,1999,127(3-4):250-266.
  • 8Chen Y,Rangarajan G,Feng F,et al.Analyzing multiple nonlinear time series with extended Granger causality[J].Physics Letters A,2004,324(1):26-35.
  • 9Feldmann U,Bhattacharya J.Predictability improvement as an asymmetrical measure of interdependence in bivariate time series[J].International Journal of Bifurcation and Chaos,2004,14(2):505-514.
  • 10Pereda E,Bhattacharya J,Quiroga R Q.Nonlinear multivariate analysis of neurophysiological signals[J].Progress in Neurobiology,2005,77(1-2):1-37.

二级参考文献47

共引文献117

同被引文献54

引证文献4

二级引证文献53

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部