Abstract Locally weighted partial least squares (LWPLS) is a widely used just-in-time learning (JITL) modeling algorithm for adaptive soft sensor development. In LWPLS, spatial variable distance is used to measure similarity and assign weights for historical samples, which is very effective to handle process time-varying problems of abrupt changes. However, the gradual process changes are not effectively handled in traditional LWPLS. To cope with this problem, a novel similarity is proposed for temporal distance measurement by introducing a temporal variable of sampling instant, in which newest sampled data can get large weights since they represent the more recent process state. Then, both spatial and temporal similarities are considered to construct a spatial-temporal adaptive LWPLS modeling framework in this paper. The effectiveness of the proposed algorithm is validated on an industrial hydrocracking process.

中文翻译：

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用于自适应软传感器建模的时空 LWPLS 及其在工业加氢裂化过程中的应用

摘要 局部加权偏最小二乘法 (LWPLS) 是一种广泛用于自适应软传感器开发的即时学习 (JITL) 建模算法。在LWPLS中，利用空间变量距离来度量历史样本的相似性和分配权重，对于处理突变的过程时变问题非常有效。然而，在传统的 LWPLS 中并没有有效地处理渐进的过程变化。为了解决这个问题，通过引入采样时刻的时间变量，为时间距离测量提出了一种新的相似性，其中最新的采样数据可以得到很大的权重，因为它们代表了最近的过程状态。然后，本文考虑空间和时间的相似性来构建时空自适应 LWPLS 建模框架。