Abstract
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 Short Term 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.