一个数据驱动的框架来学习混合动力系统
Yang Li1, Shengyuan Xu1, Jinqiao Duan2
1School of Automation, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing 210094, China.
Chaos (Woodbury, N.Y.)
|June 22, 2023
概括
本研究引入了一种新的数据驱动方法,用于从时间序列数据中发现混合动态系统,而不需要先前的系统知识. 该框架有效地学习复杂系统的规律,提供广泛的适用性.
科学领域:
- 动态系统理论 动态系统理论
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 混合动力系统的现有数据驱动方法通常需要对模型结构的先验知识.
- 参数识别通常仅限于预定义的函数或规定的形式.
研究的目的:
- 开发一种新的数据驱动框架,直接从时间序列数据中发现混合动力系统.
- 消除对系统底层结构或功能事先知识的需求.
主要方法:
- 采用双循环算法来隔离属于单个子系统的数据.
- 剩余网络被代训练以接近子系统动态.
- 一个完全连接的神经网络估计子系统之间的过渡规则.
主要成果:
- 提出的方法成功地识别了跨各种维度和结构的混合动力系统.
- 在几个原型实例上证明了有效性和准确性.
- 该框架从数据中学习进化规律.
结论:
- 这种新的框架为学习混合动力系统提供了有效的工具,不需要先前的知识.
- 该方法显示了从可用的数据集分析复杂系统的广泛适用性.
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