Soft sensor are widely used to estimate process variables which are difficult to measure
online in industrial process control. This paper proposes a new soft sensor modeling method …

Soft sensors have been widely used in industrial processes. The core issue of data-driven
soft sensors is building soft sensor models with excellent performance and robustness. This …

In industrial process control, some product qualities and key variables are always difficult to
measure online due to technical or economic limitations. As an effective solution, data …

Deep learning has been proposed for soft sensor modeling in process industries. However,
conventional deep neural network (DNN) is a static network and thereby can not embrace …

This paper proposes a semisupervised deep neural regression network with embedding
manifold (SSE-DNN) for soft sensor modeling that integrates manifold embedding into deep …

The soft sensor modeling method based on support vector machine (SVM) has been widely
used in the field of industrial process control. However, the traditional support vector …

The soft sensor modeling method based on support vector machine (SVM) has been widely
used in the field of industrial process control. However, the traditional support vector …

Soft sensors have been extensively used to predict difficult-to-measure quality variables for
effective modeling, control and optimization of industrial processes. To construct accurate …

Soft sensors have become reliable tools for estimating difficult-to-measure target variables in
modern industrial processes. In order to make full use of labeled and unlabeled samples, an …

Nowadays, data-driven soft sensors have been developed to estimate the quality variables
which are difficult-to-measure in industrial processes. Feature representation plays a …