Double locally weighted principal component regression for soft sensor with sample selection under supervised latent structure

Abstract

Just-in-time learning is an important framework for adaptive soft sensor modeling. However, the prediction accuracy strongly depends on the appropriate selection of relevant samples. Most traditional sample selection methods only consider the input information of samples, without any reference to the output information. In this paper, a new sample selection method in the supervised latent structure is proposed, in which the latent variables are highly related to the output. After that, to enhance the performance of the JITL local model, both sample importance and variable importance are taken into consideration for feature extraction in principal component regression (PCR), which is termed as double locally weighted principal component regression (D-LWPCR) in this paper. The effectiveness of the D-LWPCR model with sample selection in supervised latent structure is validated through a numerical example and an industrial blast furnace ironmaking process.