Innovative Solutions for Design and Fabrication of Deep Learning Based Soft Sensor

Soft sensors are used to anticipate complicated model parameters
using data from classifiers that are comparatively easy to gather.
The goal of this study is to use artificial intelligence techniques to
design and build soft sensors. The combination of a Long ShortTerm Memory (LSTM) network and Grey Wolf Optimization
(GWO) is used to create a unique soft sensor. LSTM is developed
to tackle linear model with strong nonlinearity and
unpredictability of manufacturing applications in the learning
approach. GWO is used to accomplish input optimization
technique for LSTM in order to reduce the model's inappropriate
complication. The newly designed soft sensor originally brought
LSTM's superior dynamic modeling with GWO's exact variable
selection. The performance of our proposal is demonstrated using
simulations on real-world datasets.