从度测量中描述复杂的时空模式
Luan Orion Barauna1, Rubens Andreas Sautter1, Reinaldo Roberto Rosa1,2
1Applied Computing Graduate Program (CAP), National Institute for Space Research, Av. dos Astronautas, 1.758, Jardim da Granja, São José dos Campos 12227-010, SP, Brazil.
Entropy (Basel, Switzerland)
|June 26, 2024
概括
这项研究引入了一种基于的新方法来分类复杂的时空模式. 该方法有效地区分了各种动态过程,如流和噪声,使用香农变量 (SHp) 和萨利斯光谱变量 (Sqs).
科学领域:
- 复杂系统分析 复杂系统分析
- 统计热力学 统计热力学
- 时间序列分析时间序列分析
背景情况:
- 概率测量对于分析复杂系统和时间序列至关重要.
- 目前的方法需要进一步开发用于二维和三维数据.
- 时空过程的分类仍然是一个挑战.
研究的目的:
- 开发一种使用度测量来分类时空过程的新方法.
- 通过区分五类随机模式来验证该方法.
- 为了确定最佳的度措施,以提高分类性能.
主要方法:
- 选择了五类随机模式:白色噪声,红色噪声,反应扩散,水力动态流和等离子流 (MHD).
- 从矩阵中评估了七种测量技术.
- 开发了一个参数空间,使用两个最有效的度:香农变量 (SHp) 和萨利斯光谱变量 (Sqs).
主要成果:
- SHp×Sqs参数空间有效地分离了五类时空过程.
- 香农变量 (SHp) 和萨利斯光谱变量 (Sqs) 显示出优异的组合性能.
- 对于每个动态过程类,在SHp×Sqs空间内确定了特定的部门.
结论:
- 拟议的基于的方法提供了一个强大的方法来分类复杂的时空模式.
- SHp×Sqs参数空间提供了一个强大的工具,用于区分不同的动态过程.
- 这种方法可以用来训练机器学习模型进行自动的时空模式分类.
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