用于机器学习分类的重复微态
G S Spezzatto1, J V V Flauzino1, G Corso2
1Department of Physics, Federal University of Paraná, 81531-980 Curitiba, Brazil.
Chaos (Woodbury, N.Y.)
|July 19, 2024
概括
复制微态是一种新型的复制量计,可以有效地检测微妙的数据模式变化. 这些微状态增强机器学习模型,如微状态多层感知子 (MMLP),以更准确地对混乱系统进行分类.
科学领域:
- 复杂系统分析 复杂系统分析
- 时间序列数据挖掘时间序列数据挖掘
- 机器学习 机器学习
背景情况:
- 反复量化分析 (RQA) 传统上分析时间序列动态.
- 阶段空间重复是动态系统理论的一个基本概念.
- 在现有的RQA方法中检测微妙的模式变化存在局限性.
研究的目的:
- 引入复发微态作为相位空间复发的概括.
- 开发一种新的功能生成工具,用于从时间序列数据中进行机器学习.
- 使用深度神经网络提高混乱系统参数的分类.
主要方法:
- 从嵌入式值序列的交叉递归中获得递归微态.
- 使用微状态发生概率作为复发量化器.
- 实现微态多层感知器 (MMLP) 用于参数分类.
主要成果:
- 微态概率检测到数据模式的微妙变化.
- MMLP有效地对混乱系统的参数进行了分类.
- 增加微态的数量可以提高MMLP分类的准确性.
结论:
- 递归微态为时间序列分析提供了一种敏感而富有信息性的方法.
- 在分类混乱系统参数方面,MMLP表现出强的表现.
- 该方法显示了在不同的数据环境中各种应用的潜力.
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