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A Novel Just-in-Time Learning Strategy for Soft Sensing with Improved Similarity Measure Based on Mutual Information and PLS

机译：一种新的柔和传感的立交式学习策略基于相互信息和PLS改进的相似度量

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摘要

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.
机译：在现代工业过程控制中，基于即时学习（JITL）的软传感器已被广泛应用。精确的相似性度量在基于JITL的软传感器建模中至关重要，因为它不仅是选择最近邻居样本的基础，还确定了样本权重。近年来，JITL相似度测量方法已经大大富集，包括基于欧几里德距离的方法，加权欧几里德距离，相关等。然而，由于输入变量对输出的影响不同，输入和输出之间的复杂非线性关系，输入变量与其他复杂因素之间的共同性，上述相似度测量方法可能变得不准确。在本文中，通过组合互信息（MI）和局部最小二乘（PLS）来提出新的相似度测量方法。在本研究中设计了一个两阶段计算框架，包括培训阶段和预测阶段，以减少在线计算负担。在预测阶段，为了建立本地模型，采用具有变量和样品的改进的局部加权PLS（LWPLS）被采用双重加权。上述操作构成了一种新的JITL建模策略，其名为MI-PLS-LWPLS。通过与其他相关的JITL方法进行比较，通过对合成弗里德曼数据集和真正的工业数据集进行案例研究，通过案例研究验证了MI-PLS-LWPLS方法的有效性。

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