从单变时间序列中对卡尔曼运算子的估计
Sherehe Semba1,2, Huijie Yang1, Xiaolu Chen3
1Department of Systems Science, Business School, University of Shanghai for Science and Technology, Shanghai 200093, China.
本研究引入了一种新的数据驱动方法来重建非线性动态系统,即使有隐藏变量. 该技术通过时间序列数据准确地估计了系统运营商.
科学领域:
- 动态系统理论 动态系统理论
- 数据驱动科学数据驱动科学
- 非线性动力学是一种非线性动力学.
背景情况:
- 从时间序列中重建非线性动态系统对于数据驱动分析至关重要.
- 隐藏变量在准确建模这些系统方面构成了重大挑战.
- 现有的方法经常在不完整的观测数据下扎.
研究的目的:
- 从有限的时间序列数据重建非线性动态系统的综合数据驱动方法.
- 为了应对系统识别中未观察到 (隐藏) 变量的挑战.
- 为了准确估计非线性动态系统的卡尔曼运算子.
主要方法:
- 卡尔曼线性化,相空间嵌入 (由塔肯定理提供信息) 和动态模式分解的集成.
- 阶段空间嵌入确定系统的维度基于可观察的变量.
- 卡尔曼线性化将非线性系统转化为无限线性系统,通过动态模式分解来截断.
主要成果:
- 综合技术从时间序列数据准确地重建非线性动态系统.
- 该方法在各种模型中显示出强大的性能,包括洛伦茨模型和Duffing振荡器.
- 对心电图,脑电图和麻疹疫情的经验数据的成功应用验证了这一方法.
结论:
- 本文介绍了一种新的数据驱动方法,用于估计非线性动态系统的卡尔曼运算子.
- 该方法有效地处理带有隐藏变量的系统,提供准确的系统运营商估计.
- 这些发现为分析各种科学领域的复杂系统提供了有价值的工具.
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