arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1102.1853nlin.CD
收藏

混沌动力学的几何学--复杂网络的观点

The Geometry of Chaotic Dynamics -- A Complex Network Perspective

Reik V. Donner, Jobst Heitzig, Jonathan F. Donges, Yong Zou, Norbert Marwan, Jürgen Kurths

摘要:

最近,已经开发了几种用于时间序列分析的复杂网络方法,并应用于研究广泛的模型系统以及真实世界数据,例如,地球物理或金融时间序列。在这些技术中,基于递归的概念和显着的$ε$递归网络,最忠实地表示混沌(和不太有趣的非混沌)时间序列的吸引子的几何精细结构。在本文中,我们证明了著名的图论性质,局部聚类系数和全球(网络)传递性可以有意义地利用定义两个新的本地和两个新的全球措施相空间中的尺寸:本地上,下聚类维数以及全球上,下传递性维数。严格的分析以及数值计算结果的自相似集和简单的混沌模型系统表明,这些措施是良好的行为,在大多数非病理情况下,他们可以合理地估计以及使用$ε$递归网络从相对较短的时间序列。此外,我们一方面研究了聚类和传递性维数之间的关系,另一方面研究了传统的度量方法,如逐点维数或局部李雅普诺夫维数。我们还提供了进一步的证据表明,当地的聚类系数,或相当于当地的聚类维数,是有用的,用于识别不稳定的周期轨道和其他动态不变的对象从时间序列。我们的研究结果表明,$ε$-递归网络表现出动力系统和图论之间的重要联系。

英文摘要:

Recently, several complex network approaches to time series analysis have been developed and applied to study a wide range of model systems as well as real-world data, e.g., geophysical or financial time series. Among these techniques, recurrence-based concepts and prominently $ε$-recurrence networks, most faithfully represent the geometrical fine structure of the attractors underlying chaotic (and less interestingly non-chaotic) time series. In this paper we demonstrate that the well known graph theoretical properties local clustering coefficient and global (network) transitivity can meaningfully be exploited to define two new local and two new global measures of dimension in phase space: local upper and lower clustering dimension as well as global upper and lower transitivity dimension. Rigorous analytical as well as numerical results for self-similar sets and simple chaotic model systems suggest that these measures are well-behaved in most non-pathological situations and that they can be estimated reasonably well using $ε$-recurrence networks constructed from relatively short time series. Moreover, we study the relationship between clustering and transitivity dimensions on the one hand, and traditional measures like pointwise dimension or local Lyapunov dimension on the other hand. We also provide further evidence that the local clustering coefficients, or equivalently the local clustering dimensions, are useful for identifying unstable periodic orbits and other dynamically invariant objects from time series. Our results demonstrate that $ε$-recurrence networks exhibit an important link between dynamical systems and graph theory.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑