Kong Li, Pan Hao, Li Xuewei, Ma Shuangbao, Xu Qi, Zhou Kaibo
School of Artificial Intelligence and Automation, Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, China.
School of Intelligent Engineering, Henan Institute of Technology, Xinxiang 453003, China.
Entropy (Basel). 2019 Jul 15;21(7):691. doi: 10.3390/e21070691.
Measurement is a key method to obtain information from the real world and is widely used in human life. A unified model of measurement systems is critical to the design and optimization of measurement systems. However, the existing models of measurement systems are too abstract. To a certain extent, this makes it difficult to have a clear overall understanding of measurement systems and how to implement information acquisition. Meanwhile, this also leads to limitations in the application of these models. Information entropy is a measure of information or uncertainty of a random variable and has strong representation ability. In this paper, an information entropy-based modeling method for measurement system is proposed. First, a modeling idea based on the viewpoint of information and uncertainty is described. Second, an entropy balance equation based on the chain rule for entropy is proposed for system modeling. Then, the entropy balance equation is used to establish the information entropy-based model of the measurement system. Finally, three cases of typical measurement units or processes are analyzed using the proposed method. Compared with the existing modeling approaches, the proposed method considers the modeling problem from the perspective of information and uncertainty. It focuses on the information loss of the measurand in the transmission process and the characterization of the specific role of the measurement unit. The proposed model can intuitively describe the processing and changes of information in the measurement system. It does not conflict with the existing models of the measurement system, but can complement the existing models of measurement systems, thus further enriching the existing measurement theory.
测量是从现实世界获取信息的关键方法，在人类生活中被广泛使用。测量系统的统一模型对于测量系统的设计和优化至关重要。然而，现有的测量系统模型过于抽象。在一定程度上，这使得难以对测量系统以及如何实现信息获取有清晰的整体认识。同时，这也导致这些模型在应用方面存在局限性。信息熵是对随机变量的信息或不确定性的一种度量，具有很强的表征能力。本文提出了一种基于信息熵的测量系统建模方法。首先，描述了一种基于信息和不确定性观点的建模思路。其次，提出了基于熵的链式法则的熵平衡方程用于系统建模。然后，利用熵平衡方程建立测量系统的基于信息熵的模型。最后，使用所提出的方法分析了典型测量单元或过程的三种情况。与现有的建模方法相比，所提出的方法从信息和不确定性的角度考虑建模问题。它关注被测量在传输过程中的信息损失以及测量单元具体作用的表征。所提出的模型能够直观地描述测量系统中信息的处理和变化。它与现有的测量系统模型不冲突，但可以对现有的测量系统模型进行补充，从而进一步丰富现有的测量理论。
