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拓数据分析方法用于动态系统的时间序列和形状分析
W Hussain Shah1, S Rafia Fatima2, G Huerta-Cuellar1
1Departamento de Ciencias Exactas y Tecnología, Centro Universitario de los Lagos, Universidad de Guadalajara, Enrique Díaz de León 1144, Colonia Paseos de la Montaña, Lagos de Moreno, Jalisco, Mexico.
Chaos (Woodbury, N.Y.)
|June 17, 2025
概括
拓数据分析通过分析时间序列和相位空间,为动态系统提供了新的见解. 这种方法可以使用机器学习技术对系统行为进行自动分类.
科学领域:
- 动态系统 动态系统
- 拓数据分析 拓数据分析
- 非线性动力学是一种非线性动力学.
背景情况:
- 时间序列和相位空间对于理解动态系统至关重要.
- 拓数据分析 (TDA) 为描述复杂系统提供了新的方法.
- 现有的TDA方法在直接分析动态系统特征方面存在局限性.
研究的目的:
- 将TDA应用于动态系统的时间序列和相位空间.
- 为非线性动态引入一个全面的TDA管道.
- 为了实现动态行为的自动分析和分类.
主要方法:
- 在时间序列数据上利用持久的同质性转换为点云.
- 使用Rips复合体进行同质计算.
- 首次将立方同质性应用于相位图像.
- 计算机拓机器学习的特点包括持久景观和持久图像.
主要成果:
- 从时间序列中成功测量了系统行为的拓特征.
- 开发了一种新的基于图像的方法,用于使用立方同质学的相位图分析.
- 生成机器学习功能,用于动态行为的自动分类.
- 证明了TDA在分析Rössler类吸引子中的实用性.
结论:
- TDA提供了一个强大的框架来分析动态系统.
- 开发的管道使得TDA可用于非线性动力学研究.
- 将TDA与机器学习集成起来,便于自动化行为分类.
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