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通过空间递归特性识别复杂时空系统的动态。

Identifying the dynamics of complex spatio-temporal systems by spatial recurrence properties.

机构信息

Dipartimento di Ingegneria dell'Informazione, Università di Siena, Centro per lo Studio dei Sistemi Complessi, via Roma 56, 53100 Siena, Italy.

出版信息

Proc Natl Acad Sci U S A. 2010 May 4;107(18):8097-102. doi: 10.1073/pnas.0910414107. Epub 2010 Apr 19.

Abstract

Complex spatio-temporal systems may exhibit irregular behaviors when driven far from equilibrium. Reaction-diffusion systems often lead to the formation of patterns and spatio-temporal chaos. When a limited number of observations is available, the reconstruction and identification of complex dynamical regimes become challenging problems. A method based on spatial recurrence properties is proposed to deal with this problem: generalized recurrence plots and generalized recurrence quantification analysis are exploited to show that detection of structural changes in spatially distributed systems can be performed by setting up appropriate diagrams accounting for different spatial recurrences. The method has been tested on two prototypical systems forming complex patterns: the complex Ginzburg-Landau equation and the Schnakenberg system. This work allowed us to identify changes in the stability of spiral wave solutions in the former system and to analyze the Turing bifurcations in the latter.

摘要

复杂的时空系统在远离平衡态时可能表现出不规则的行为。反应扩散系统通常会导致模式和时空混沌的形成。当可用的观测数量有限时,复杂动态状态的重建和识别就成为具有挑战性的问题。本文提出了一种基于空间递归特性的方法来解决这个问题:利用广义递归图和广义递归量化分析表明,通过设置适当的图表来考虑不同的空间递归,可以对空间分布系统中的结构变化进行检测。该方法已经在两个形成复杂模式的原型系统上进行了测试:复杂的 Ginzburg-Landau 方程和 Schnakenberg 系统。这项工作使我们能够识别前一个系统中螺旋波解稳定性的变化,并在后一个系统中分析 Turing 分支。

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