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Ensemble locally weighted partial least squares as a just‐in‐time modeling method
Author(s) -
Kaneko Hiromasa,
Funatsu Kimito
Publication year - 2016
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.15090
Subject(s) - hyperparameter , partial least squares regression , set (abstract data type) , ensemble learning , soft sensor , data set , process (computing) , computer science , bayes' theorem , least squares function approximation , ensemble forecasting , artificial intelligence , variable (mathematics) , mathematics , data mining , machine learning , algorithm , statistics , bayesian probability , estimator , programming language , operating system , mathematical analysis
The predictive ability of soft sensors, which estimate values of an objective variable y online, decreases due to process changes in chemical plants. To reduce the decrease of predictive ability, adaptive soft sensors have been developed. We focused on just‐in‐time soft sensors, especially locally weighted partial least squares (LWPLS) regression. Since a set of hyperparameters in an LWPLS model has to be set beforehand and there is only onedataset, a traditional LWPLS model is difficult to accurately predict y‐values in multiple process states. In this study, we propose to combine LWPLS and ensemble learning, and predict y‐values with multiple LWPLS models, whose datasets and sets of hyperparameters are different. The weights of LWPLS models are determined based on Bayes’ theorem, considering their predictive ability. We confirmed that the proposed model has higher predictive accuracy than traditional models through numerical simulation data and two industrial data analyses. © 2015 American Institute of Chemical Engineers AIChE J , 62: 717–725, 2016