School of Management, Hefei University of Technology, Hefei 230009, China.
Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009, China.
Sensors (Basel). 2020 Jul 7;20(13):3804. doi: 10.3390/s20133804.
In modern industrial process control, just-in-time learning (JITL)-based soft sensors have been widely applied. An accurate similarity measure is crucial in JITL-based soft sensor modeling since it is not only the basis for selecting the nearest neighbor samples but also determines sample weights. In recent years, JITL similarity measure methods have been greatly enriched, including methods based on Euclidean distance, weighted Euclidean distance, correlation, etc. However, due to the different influence of input variables on output, the complex nonlinear relationship between input and output, the collinearity between input variables, and other complex factors, the above similarity measure methods may become inaccurate. In this paper, a new similarity measure method is proposed by combining mutual information (MI) and partial least squares (PLS). A two-stage calculation framework, including a training stage and a prediction stage, was designed in this study to reduce the online computational burden. In the prediction stage, to establish the local model, an improved locally weighted PLS (LWPLS) with variables and samples double-weighted was adopted. The above operations constitute a novel JITL modeling strategy, which is named MI-PLS-LWPLS. By comparison with other related JITL methods, the effectiveness of the MI-PLS-LWPLS method was verified through case studies on both a synthetic Friedman dataset and a real industrial dataset.
在现代工业过程控制中，基于即时学习（JITL）的软测量技术得到了广泛应用。在基于 JITL 的软测量建模中，准确的相似度度量至关重要，因为它不仅是选择最近邻样本的基础，还决定了样本的权重。近年来，JITL 相似度度量方法得到了极大的丰富，包括基于欧几里得距离、加权欧几里得距离、相关系数等的方法。然而，由于输入变量对输出的影响不同、输入和输出之间复杂的非线性关系、输入变量之间的共线性以及其他复杂因素，上述相似度度量方法可能变得不准确。本文提出了一种新的相似度度量方法，将互信息（MI）和偏最小二乘法（PLS）相结合。本研究设计了一个两阶段计算框架，包括训练阶段和预测阶段，以降低在线计算负担。在预测阶段，为了建立局部模型，采用了变量和样本双重加权的改进局部加权 PLS（LWPLS）。上述操作构成了一种新的 JITL 建模策略，称为 MI-PLS-LWPLS。通过对合成 Friedman 数据集和真实工业数据集的案例研究，验证了 MI-PLS-LWPLS 方法的有效性。
