.png){kind=icon}
返回
Augmented Physics-Based Models for High-Order Markov Filtering
.png){kind=icon}
OA
AI
DOI：10.3390/s24186132.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Hybrid physics-based data-driven models, namely, augmented physics-based models (APBMs), are capable of learning complex state dynamics while maintaining some level of model interpretability that can be controlled through appropriate regularizations of the data-driven component. In this article, we extend the APBM formulation for high-order Markov models, where the state space is further augmented with past states (AG-APBM). Typically, state augmentation is a powerful method for state estimation for a high-order Markov model, but it requires the exact knowledge of the system dynamics. The proposed approach, however, does not require full knowledge of dynamics, especially the Markovity order. To mitigate the extra computational burden of such augmentation we propose an approximated-state APBM (AP-APBM) implementation leveraging summaries from past time steps. We demonstrate the performance of AG- and AP-APBMs in an autoregressive model and a target-tracking scenario based on the trajectory of a controlled aircraft with delay-feedback control. The experiments showed that both proposed strategies outperformed the standard APBM approach in terms of estimation error and that the AP-APBM only degraded slightly when compared to AG-APBM. For example, the autoregressive (AR) model simulation in our settings showed that AG-APBM and AP-APBM reduced the estimate error by 31.1% and 26.7%. The time cost and memory usage were reduced by 37.5% and 20% by AP-APBM compared to AG-APBM.
Keyword:
nonlinear filtering
high-order Markov
hybrid neural network
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Molecular motion in poly(amino acid)s: 7. Molecular motion in the β-form crystal of silk fibre.png)
Polymer
PolymerIF0
PREAI
分享收藏The network of patterns: creating a design guide using Christopher Alexander's pattern language模式网络: 使用Christopher Alexander的模式语言创建设计指南
PREAI
分享收藏Distributed cubature H-infinity information filtering for target tracking against uncertain noise statisticsSIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏Metal–organic frameworks as heterogeneous photocatalysts: advantages and challengesCrystEngCommOAAI
CrystEngCommIF0
OAAI分享收藏Using higher-order Markov models to reveal flow-based communities in networksSCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏
查看更多内容