极端事件的复杂性衡量标准
Dhiman Das1, Arnob Ray1,2, Chittaranjan Hens3
1Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India.
Chaos (Woodbury, N.Y.)
|December 2, 2024
概括
这项研究引入了一种新的方法来测量混乱信号中极端事件的复杂性. 这些发现表明,这种复杂度度可以区分极端混乱与非极端混乱.
科学领域:
- 非线性动力学是一种非线性动力学.
- 信号处理 信号处理
- 复杂性科学是一门复杂性科学.
背景情况:
- 复杂性测量对于描述不规则信号至关重要.
- 现有的方法往往侧重于一般的混乱,忽视极端事件复杂性.
- 在量化和区分极端混乱事件方面存在差距.
研究的目的:
- 量化和比较极端事件的复杂性与非极端混乱信号.
- 开发一种能够区分这两种信号类型的方法.
- 探索导致极端事件的过渡动态.
主要方法:
- 使用正常化的农与不平衡相结合.
- 分析不同系统参数的信号复杂性.
- 使用三个不同的动态系统:Liénard系统,Ikeda地图和Hindmarsh-Rose系统.
主要成果:
- 和不平衡的综合测量成功地将极端与非极端的混乱区分开来.
- 该方法确定了过渡点,包括波莫-曼尼维尔间歇性和内部危机.
- 一般的复杂性趋势显示,在过渡到极端事件的过程中,复杂性趋势会增加,达到峰值,然后下降.
结论:
- 拟议的复杂性测量是有效区分极端混乱事件.
- 这种方法为信号向极端行为转变的动态提供了洞察力.
- 这些发现在多个复杂的动态系统中得到了验证.
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