的结构作为一个测试床,用于序列模式的测量
Yong Zou1, Norbert Marwan2,3, Xiujing Han4
1School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China.
Chaos (Woodbury, N.Y.)
|December 17, 2024
概括
顺序模式转换网络 (OPTNs) 为分析复杂的动态系统提供了一种新的方法. 这种方法有效地区分时间序列数据中的混乱和周期性行为,优于传统技术.
科学领域:
- 动态系统和混沌理论
- 复杂网络分析 复杂网络分析
- 时间序列分析时间序列分析
背景情况:
- 对于传统的时间序列分析来说,在混乱的参数空间中描述复杂的周期窗口是一个挑战.
- 动态系统中的结构表现出明显的分叉和通向混乱的路径,例如周期翻倍和间歇性.
- 现有的复杂网络方法很难从数值上捕捉到这些动态,特别是周期翻倍路线.
研究的目的:
- 引入和评估顺序模式转换网络 (OPTNs) 用于在动态系统中表征结构.
- 为了利用顺序模式之间的过渡行为来增强动态信息提取.
- 为了比较OPTN与传统顺序测量的有效性,以区分混沌和周期时间序列.
主要方法:
- 开发和应用顺序模式转换网络 (OPTNs) 来分析时间序列数据.
- 三个基于顺序模式的测量值的比较: permutation entropy (εO),平均幅度波动 (σ) 和OPTN外链转变 (εE).
- 使用拟议的措施,评估分类准确性,以区分混沌和周期时间序列.
主要成果:
- 顺序模式转换网络 (OPTNs) 捕获超越传统顺序措施的动态信息.
- OPTN外链过渡 (εE) 在对混沌和周期时间序列进行分类方面表现出卓越的表现.
- 在OPTN链接权重中编码的顺序模式之间的过渡频率提供了有价值的互补见解.
结论:
- 顺序模式转换网络 (OPTNs) 为分析复杂的动态系统和识别结构提供了一个强大的新工具.
- 通过OPTN链接权重捕获的顺序模式之间的过渡动态对于全面了解系统行为至关重要.
- OPTN外链转变 (εE) 代表了时间序列分析在区分复杂动态方面取得的重大进展.
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