Soft sensing or inferential estimation has long been considered a potent tool to deal with the
conflict between small control interval and large sampling interval existing in a wide variety …

Since in practical industrial applications, soft sensors based on static models often lead to
low accuracy and poor robustness, a multiple neural network (MNN) model is presented to …

Focuses on the use of a neural network-based soft-sensing concept for process variables
which cannot be measured online. An approach is proposed which performs particularly …

Soft sensor technology is an important means to estimate important process variables in real-
time. Modeling for soft sensor system is the core of this technology. Most nonlinear dynamic …

Soft-sensors assist in solving the problem created by the unavailability of a sensor by
providing a software backup for it, thus allowing a reduction of losses in plant performance …

Data-driven-based soft sensors play significant roles in predicting key quality and optimizing
the production process. Considering the difficulty and cost of variable acquisition, these …

We consider a clustering-based construction of a soft sensor with adaptive model structure
and parameters and with the time factor taken into account by the example of an industrial …

This brief develops a novel just-in-time (JIT) learning-based soft sensor for modeling of
industrial processes. The recorded data is assumed to exhibit non-Gaussian signal …

Conventional data-driven soft sensors commonly rely on the assumption that processes are
operating at steady states. As chemical processes involve evident dynamics, conventional …

The field of soft sensor development has gained significant importance in the recent past
with the development of efficient and easily employable computational tools for this purpose …