A Regularized Mixture of Linear Experts for Quality Prediction in Multimode and Multiphase Industrial Processes

Francisco Souza, Jérôme Mendes, Rui Araújo
2021 Applied Sciences  
This paper proposes the use of a regularized mixture of linear experts (MoLE) for predictive modeling in multimode-multiphase industrial processes. For this purpose, different regularized MoLE were evaluated, namely, through the elastic net (EN), Lasso, and ridge regression (RR) penalties. Their performances were compared when trained with different numbers of samples, and in comparison to other nonlinear predictive models. The models were evaluated on real multiphase polymerization process
more » ... ization process data. The Lasso penalty provided the best performance among all regularizers for MoLE, even when trained with a small number of samples.
doi:10.3390/app11052040 fatcat:3rxcensuwnhtrkf43d67sra3o4