一个未知控制方程的物理系统的状态估计
Kevin Course1, Prasanth B Nair2
1Institute for Aerospace Studies, University of Toronto, Toronto, Ontario, Canada.
Nature
|October 11, 2023
概括
这项研究引入了一种新的贝叶斯式状态估计方法,即使系统动态未知,也可以进行准确的推断. 该方法同时学习模型组件和系统状态,扩大状态估计技术的范围.
科学领域:
- 机器学习
- 控制理论
- 应用数学
背景情况:
- 状态估计将杂的观测与系统模型相协调,从而推断出无法测量的状态.
- 传统方法假设特定的不确定性形式 (例如,增量随机或参数).
- 许多真实世界的系统有部分或完全未知的动态,限制了经典的方法.
研究的目的:
- 为具有未知动态的系统开发一个近似的贝叶斯状态估计方法.
- 能够同时学习未知的模型术语和系统状态.
- 将状态估计的适用性扩展到以前难以解决的问题.
主要方法:
- 用马尔科夫高斯过程进行随机变量推理的重组策略.
- 一个近似的贝叶斯框架来学习未知的系统动态.
- 同时估计系统状态和模型参数.
主要成果:
- 提出的方法有效地对部分或完全未知的系统演化方程进行状态估计.
- 它同时学习数学模型中的缺失项和估计状态.
- 展示了处理状态估计中的模型不确定性的新方法.
结论:
- 开发的技术推进了对状态估计的近似贝叶斯推理.
- 它显著扩大了国家估计所能解决的问题范围.
- 随机变化推断的进步对机器学习有更广泛的意义.
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