CEBoosting:在线稀疏识别动态系统,通过因果关系来切换模式,以促进
Chuanqi Chen1, Nan Chen2, Jin-Long Wu1
1Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA.
Chaos (Woodbury, N.Y.)
|August 7, 2023
概括
本研究介绍了一种因果关系增强 (CEBoosting) 策略,用于检测复杂系统中的模式切换. 该方法有效地使用因果指标识别动态变化,即使在有限的数据上也证明了强大.
科学领域:
- 复杂系统动力学 复杂系统动力学
- 非线性系统分析 非线性系统分析
- 数据驱动建模数据驱动建模
背景情况:
- 调节切换在复杂的动态系统中很常见,其特点是多级特征,混乱和极端事件.
- 检测这些转变和理解相关的动态对于系统分析至关重要.
研究的目的:
- 开发一种新的策略,即因果增强 (CEBoosting),用于在线检测政权切换.
- 通过在线模型识别发现新出现的政权的潜在动态.
主要方法:
- 使用因果作为预定义库中的候选函数的逻辑值.
- 使用因果关系逆转指标来信号模式切换.
- 实现参数估计作为二次优化问题,用分析公式解决.
主要成果:
- 证明了因果关系指标的有效计算以及对大规模系统的适用性 (例如,洛伦兹96模型).
- 展示了CEBoosting算法的适应性,以部分观测和集成与数据同化用于隐藏过程触发的切换.
- 成功地将CEBoosting应用于非线性地形平均流量交互模型,用于在间歇性和极端事件中在线检测调节切换.
结论:
- CEBoosting提供了一种强大而高效的方法,用于在复杂系统中在线调节切换检测和动态发现.
- 该策略具有多功能性,可以处理部分观测并与数据同化进行整合.
- 即使在具有强烈间歇性和极端事件的系统中,CEBOosting也有效.
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