论文标题
基于信息的数字双胞胎行为匹配的基于信息的模型歧视
Information-Based Model Discrimination for Digital Twin Behavioral Matching
论文作者
论文摘要
Digital Twin是一项破坏技术，它允许根据系统及其物理定律的最新信息创建复杂物理系统的虚拟表示。但是，由于每个双胞胎中未知参数的数量，使数字双胞胎行为与真实系统匹配可能会具有挑战性。它可以使用基于优化的技术进行搜索，并基于不同的系统数据集生产模型家族，因此，需要歧视标准来确定最佳的数字双胞胎模型。本文提出了基于信息理论的歧视标准，以确定行为匹配过程产生的最佳数字双胞胎模型。模型的信息获益被用作歧视标准。 Box-Jenkins模型用于为每个行为匹配结果定义模型家族。将提出的方法与其他基于信息的指标以及$ν$ GAP度量进行了比较。作为研究案例，将歧视方法应用于数字双胞胎，以实现实时视力反馈红外温度均匀性控制系统。获得的结果表明，基于信息的方法对于选择代表植物家族中系统的精确数字双胞胎模型很有用
Digital Twin is a breaking technology that allows creating virtual representations of complex physical systems based on updated information of the system and its physical laws. However, making the Digital Twin behavior matching with the real system can be challenging due to the number of unknown parameters in each twin. Its search can be done using optimization-based techniques, producing a family of models based on different system datasets, so, a discrimination criterion is required to determine the best Digital Twin model. This paper presents an information theory-based discrimination criterion to determine the best Digital Twin model resulting from a behavioral matching process. The information gain of a model is employed as a discrimination criterion. Box-Jenkins models are used to define the family of models for each behavioral matching result. The proposed method is compared with other information-based metrics as well as the $ν$gap metric. As a study case, the discrimination method is applied to the Digital Twin for a real-time vision feedback infrared temperature uniformity control system. Obtained results show that information-based methodologies are useful for selecting an accurate Digital Twin model representing the system among a family of plants
