基于物理的增强模型用于高阶马尔科夫过
Shuo Tang1, Tales Imbiriba1, Jindřich Duník2
1Electrical and Computer Engineering Department, Northeastern University, Boston, MA 02115, USA.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
我们为高阶马尔科夫模型引入了基于物理的增强模型 (APBM),增强了状态估计. 我们的新方法减少了复杂动态系统中的估计误差和计算成本.
科学领域:
- 控制理论 控制理论
- 机器学习 机器学习
- 动态系统 动态系统
背景情况:
- 基于物理学的增强模型 (APBM) 将物理定律与可解释模型的数据驱动方法相结合.
- 高级马尔科夫模型需要状态增强来准确地估计状态,通常需要对系统动态的完整知识.
研究的目的:
- 使用状态增强 (AG-APBM) 将APBM扩展到高阶马尔科夫模型.
- 开发一个近似状态APBM (AP-APBM),以减少计算负担.
- 评估AG-APBM和AP-APBM的性能与标准APBM相比.
主要方法:
- 为高阶马尔科夫模型 (AG-APBM) 增加状态空间以过去的状态.
- 实现一个近似状态APBM (AP-APBM) 使用过去时间步骤总结.
- 在自回归和目标追踪场景中测试模型,并延迟反控制.
主要成果:
- 在减少估计误差方面,AG-APBM和AP-APBM都超过了标准APBM.
- AG-APBM将自回归模型估计误差减少了31.1%;AP-APBM将其减少了26.7%.
- 与AG-APBM相比,AP-APBM实现了时间成本 (37.5%) 和内存使用率 (20%) 的显著降低.
结论:
- 拟议的AG-APBM和AP-APBM有效地处理高阶马尔科夫模型,而不需要完全的动态知识.
- AP-APBM提供了一个计算效率高的替代AG-APBM,性能降低最小.
- 这些方法在复杂的控制系统中提高了状态估计的准确性和效率.
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