Ensemble regularized local finite impulse response models and soft sensor application in nonlinear dynamic industrial processes

摘要

This paper proposes a novel adaptive soft sensor modeling method based on ensemble of regularized local finite impulse response (FIR) models, which are estimated by using the stable kernel-based regularized least squares (SKRLS), for quality prediction of nonlinear dynamic industrial processes, referred to as the ELFIR-SKRLS. Meanwhile, the proposed ELFIR-SKRLS approach provides an effective way to extend the FIR-SKRLS for nonlinear dynamic system under the ensemble learning framework, and the tricky question of model order selection can be avoided. To deal with the process nonlinearity, an improved adaptive local domain partition method is employed to adaptively partition the historical data set into multiple local domains, where the optimal benchmark window selection mechanism is proposed to ensure the samples within the benchmark window belong to the same process state. In addition, a novel prediction performance-based adaptive ensemble method is proposed to combine the outputs of local FIR models, which is based on the weighted Euclidean distance-based similarity criterion and a strategy of reducing the online computation burden. The effectiveness of the proposed ELFIR-SKRLS adaptive soft sensor is demonstrated through a sulfur recover unit process application and a ladle furnace steel refining process application. (C) 2019 Elsevier B.V. All rights reserved.