Abstract Process data have a number of characteristics, such as noise, nonlinearity, process dynamics, and autocorrelation. Ideally, adaptive soft sensors would be used to solve model degradation problems resulting from changes in process characteristics. However, it is necessary to optimize the hyperparameters for each model and, depending on the state of the process, the optimal hyperparameters will change. In this study, we focus on a Gaussian process dynamical model (GPDM), a dimension-reduction method that considers all of the data characteristics. We combine a just-in-time (JIT) model and ensemble learning, and then predict y-values with multiple JIT models that have different sets of hyperparameters. Each JIT model is constructed using latent variables obtained by the GPDM. The weights of the JIT models are determined based on Bayes’ theorem in consideration of their predictive ability. Analysis of two industrial datasets confirms that the proposed model is more accurate than existing approaches.

中文翻译：

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基于用于非线性和动态过程的高斯过程动力学模型的集成即时模型

摘要 过程数据具有许多特征，例如噪声、非线性、过程动力学和自相关。理想情况下，自适应软传感器将用于解决因过程特性变化而导致的模型退化问题。但是，有必要为每个模型优化超参数，并且根据过程的状态，最佳超参数会发生变化。在这项研究中，我们专注于高斯过程动力学模型 (GPDM)，这是一种考虑所有数据特征的降维方法。我们将即时 (JIT) 模型和集成学习相结合，然后使用具有不同超参数集的多个 JIT 模型预测 y 值。每个 JIT 模型都是使用 GPDM 获得的潜在变量构建的。JIT 模型的权重是基于贝叶斯定理并考虑其预测能力来确定的。对两个工业数据集的分析证实，所提出的模型比现有方法更准确。