Model calibration method for soft sensors using adaptive gaussian process regression

Abstract

The recursive Gaussian process regression (RGPR) is a popular calibrating method to make the developed soft sensor adapt to the new working condition. Most of existing RGPR models are on the assumption that hyperparameters in the covariance function are fixed during the model calibration. In order to improve the adaptive ability of the RGPR model, hyperparameters in covariance of Gaussian process regression (GPR) are adjusted in parallel by referencing the previous optimization. The matrix inversion formula is selectively used for updating the regression model. And a dynamic offset smoother is presented to further improve the reliability of the proposed method. Applications to a numerical simulation and the penicillin fermentation process evaluate the performance of the proposed method.