The design and application of Soft Sensors (SSs) in the process industry is a growing
research field, which needs to mediate problems of model accuracy with data availability …

Soft Sensors (SSs) are inferential dynamical models employed in industries to perform
prediction of process hard-to-measure variables based on their relation with easily …

Soft sensors have been extensively developed and applied in the process industry. One of
the main challenges of the data-driven soft sensors is the lack of labeled data and the need …

Soft sensor technology has found widespread application in the real-time detection of
challenging variables like product quality and key process parameters. However, changes in …

This paper analyzes a number of strategies that are devoted to improving the generalization
capabilities of neural-network-based soft sensors when only small data sets are available …

Soft sensor has been extensively utilized in industrial processes for prediction of key quality
variables. To build an accurate virtual sensor model, it is very significant to model the …

Deep learning has been growing in popularity for soft sensor modeling of nonlinear
industrial processes, infeuality-related variables. However, applications may be highly …

On complex batch industrial processes, soft sensor modeling plays a key role in process
control and monitoring. Considering the nonlinearity, time-varying, and repetitive nature of …

Soft sensor technology is widely used in industries to handle highly nonlinear, dynamic, time-
dependent sequence data of industrial processes for predicting the key variables associated …

Data based approaches have recently gained extensive attention in modern process
industries. Accordingly, data based soft sensing technology used for making online …