The online measurement of key quality variables based on soft sensors plays a critical role in ensuring the safety and stability of batch processes. Recently, the relevant vector machine (RVM) was introduced into soft sensors for batch processes. However, the RVM‐based soft sensor has limitations in addressing the time‐varying, high‐dimensional, and dynamic data of batch processes. To address these issues, based on improved just‐in‐time learning and the relevant vector machine, an adaptive soft sensor, termed IJITL‐RVM, is proposed in this paper. The IJITL‐RVM integrates the IJITL algorithm and the RVM algorithm into a unified online modelling framework with the ability to perform adaptive updating and dynamic modelling. First, to enhance the performance of online prediction, an IJITL is designed to select modelling data based on the support vector data description (SVDD) algorithm and the kernel trick. Based on the comprehensive consideration of the strong nonlinearity and high dimensionality of process data, the IJITL can adaptively and accurately select the modelling data. Afterward, a local model is established by using the RVM for online prediction. Three applications, including a numerical simulation example, some UCI datasets, and a penicillin fermentation process, are provided to illustrate the superiority of the IJITL‐RVM‐based soft sensor.

中文翻译：

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基于改进的实时学习和相关矢量机的批处理过程软传感器开发

基于软传感器的关键质量变量的在线测量对于确保批处理过程的安全性和稳定性起着至关重要的作用。最近，相关的矢量机（RVM）被引入软传感器中以进行批处理。但是，基于RVM的软传感器在处理批处理的时变，高维和动态数据方面存在局限性。为了解决这些问题，在改进的实时学习和相关矢量机的基础上，本文提出了一种自适应软传感器，称为IJITL-RVM。IJITL-RVM将IJITL算法和RVM算法集成到一个统一的在线建模框架中，该框架具有执行自适应更新和动态建模的能力。首先，要提高在线预测的效果，IJITL旨在根据支持向量数据描述（SVDD）算法和内核技巧来选择建模数据。基于对过程数据的强非线性和高维度的综合考虑，IJITL可以自适应，准确地选择建模数据。之后，通过使用RVM建立在线预测的局部模型。提供了三个应用程序，包括一个数值模拟示例，一些UCI数据集和青霉素发酵过程，以说明基于IJITL-RVM的软传感器的优越性。使用RVM建立在线预测的本地模型。提供了三个应用程序，包括一个数值模拟示例，一些UCI数据集和青霉素发酵过程，以说明基于IJITL-RVM的软传感器的优越性。使用RVM建立在线预测的本地模型。提供了三个应用程序，包括一个数值模拟示例，一些UCI数据集和青霉素发酵过程，以说明基于IJITL-RVM的软传感器的优越性。