评价奇怪吸引子分形特征的Grassberger-Procaccia算法

基于Lorenz,Rssler和H啨ｎｏｎ三种典型的奇怪吸引子,全面分析了GrassbergerProcaccia(缩写GP)算法,详细讨论了采样数据量、延迟时间、重构相空间维数和线性区长度等参数对计算关联维数和Kolmogorov熵的影响,结果表明这些关键参数是相互关联的.通过分析关联积分谱的变化趋势,发现延迟时间与重构相空间维数对连续动力系统和离散动力系统的作用效果是不同的,且选择最佳延迟时间对计算关联维数的意义不大.指出了实际中应用GP算法应注意的问题 关键词： 奇怪吸引子 GrassbergerProcaccia算法 关联维数 Kolmogorov熵

Grassberger-Procaccia algorithm for evaluating the fractal characteristic of strange attractors

Based on the three general strange attractors generated by the Lorenz equation, the Rssler equation and the Hénon map, the Grassberger-Procaccia algorithm is analyzed. For a finite time series, the sampling number, delay time, embedding dimension and the length of scaling region affect the precision of evaluating the correlation dimension D—2 and the 2nd-order Kolmogorov entropy K—2 by G-P algorithm. In the analysis of the trend of a correlation integral, the impression for a continuous dynamical system is different from that of a discrete dynamical system in delay time and embedding dimension. The criterion of delay time chosen by mutual information is unnecessary for calculating the correlation dimension D—2. The applicable conditions for G-P algorithm is also indicated.