Abstract Just-in-time (JIT) learning based soft sensors have been widely used for predicting
product quality variables of nonlinear processes. They dynamically build online local models …

This paper develops a novel just-in-time learning (JITL) based soft sensor for multimode
processes. The involved multimode data sets are assumed to be non-Gaussian distributed …

Just-in-time learning (JITL) has recently been used for online soft sensor modeling. Unlike
traditional global approaches, the JITL-based method employs a local model built from …

Just‐in‐time learning (JITL) technique has been widely used for adaptive soft sensing of
nonlinear processes. It builds online local model with the most relevant samples from …

Just-in-time Learning (JITL) is a soft measurement method commonly used in industrial
processes, which can update local models in real-time to solve the problem of inaccurate …

In modern batch processes, soft sensors have been widely used for estimating quality
variables. However, they do not show superior prediction performance owing to the self …

Just-in-time learning (JIT) has recently gained growing popularity for soft sensor
development of nonlinear processes. However, traditional JIT methods aim to pursue a …

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 …

Soft sensor technique has become a promising solution to enable real-time estimations of
difficult-to-measure quality variables in industrial processes. However, traditional soft sensor …

Just-in-time learning (JITL) is a commonly used technique for industrial soft sensing of
nonlinear processes. However, traditional JITL approaches mainly focus on equal sample …