时间延迟的复发:一种数据驱动的方法来估计动态系统的可预测性
Chenyu Dong1, Davide Faranda2,3,4, Adriano Gualandi5,6
1Department of Mechanical Engineering, National University of Singapore, Singapore 117575, Singapore.
概括
本研究引入了一种新的数据驱动方法来分析非线性动态系统. 基于反复性的方法有效地估计了复杂系统中的局部可预测性,即使有噪音数据.
科学领域:
- 复杂系统科学 复杂系统科学
- 非线性动力学是一种非线性动力学.
- 大气科学 大气科学
背景情况:
- 非线性动态系统很普遍,但由于对初始条件和多层次过程的敏感性,难以预测.
- 传统的方法,如莱普诺夫频谱分析,需要对动态前置运算符的知识,这通常是未知的,或者通过杂的数据表现得不好.
研究的目的:
- 提出一种数据驱动的方法来分析动态系统的局部可预测性.
- 为了证明基于复发的方法对估计局部可预测性的有效性.
- 探索可预测性和其与信息理论的关系的规模依赖性.
主要方法:
- 一种基于数据的方法,基于复发的概念.
- 适用于理想化的系统和现实世界的大气场数据集.
- 分析该方法与局部动态指数和信息理论的关系.
主要成果:
- 拟议的方法有效地估计了理想化和现实世界复杂系统中的局部可预测性.
- 这种方法揭示了可预测性的规模依赖性.
- 在复杂系统中实时应用和诊断使用的证明潜力.
结论:
- 基于循环的方法为分析非线性动态系统中的局部可预测性提供了一个强大的工具.
- 它克服了传统方法的局限性,因为不需要了解动态期货运营商.
- 该方法提供了对规模依赖可预测性的见解,并在复杂系统分析中具有广泛的应用.
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