The point correlation dimension: performance with nonstationary surrogate data and noise.
The point correlation dimension: performance with nonstationary surrogate data and noise.
复制标题
点相关维度：非平稳代理数据和噪声的性能。
DOI：
10.1007/bf02691327
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发表时间：
1994
期刊：
影响因子：
--
通讯作者：
Tomberg,C
中科院分区：
文献类型：
--
作者：
Skinner,JE;Molnar,M;Tomberg,C
许多生物系统的动力学最近被归因于低维混沌，而不是以前认为的高维噪声。由于生物数据总是非平稳的，特别是在长时间间隔记录时，传统的低维混沌测量（例如，相关维算法）不能应用。为了解决这一基本问题，提出了一种新的点校正维数（PD2i）算法。在本文中，我们描述了算法的细节，并证明了局部平均pd2i可以准确地跟踪非平稳代理数据的维度。
The dynamics of many biological systems have recently been attributed to low-dimensional chaos instead of high-dimensional noise, as previously thought. Because biological data are invariably nonstationary, especially when recorded over a long interval, the conventional measures of low-dimensional chaos (e.g., the correlation dimension algorithms) cannot be applied. A new algorithm, the point correction dimension (PD2i) was developed to deal with this fundamental problem. In this article we describe the details of the algorithm and show that the local mean PD2iwill accurately track dimension in nonstationary surrogate data.