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

• Autor(a) principal: Souza, Francisco
• Data de Publicação: 2021
• Outros Autores: Mendes, Jérôme, Araújo, Rui
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/103702; https://doi.org/10.3390/app11052040
• topic: multimode process; multiphase process; mixture of experts; polymerization

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 data. The Lasso penalty provided the best performance among all regularizers for MoLE, even when trained with a small number of samples.