文献类型：期刊文献

英文题名：Gaussian Process Regression With Maximizing the Composite Conditional Likelihood

作者：Huang, Haojie[1];Li, Zhongmei[1];Peng, Xin[1];Ding, Steven X.[2];Zhong, Weimin[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai, Peoples R China;[2]Univ Duisburg Essen, Inst Automat Control & Complex Syst AKS, D-47057 Duisburg, Germany

年份：2021

卷号：70

外文期刊名：IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

收录：;EI(收录号：20213310783794);WOS:【SCI-EXPANDED(收录号:WOS:000693607800012)】；

基金：This work was supported in part by the National Natural Science Foundation of China (Basic Science Center Program) under Grant 61988101, in part by the National Natural Science Foundation of China under Grant 61925305 and Grant 61890930-3, and in part by the International (Regional) Cooperation and Exchange Project under Grant 61720106008.

语种：英文

外文关键词：Bayesian method; Gaussian process regression (GPR); hydrocracking process

摘要：Gaussian process regression (GPR) has an outstanding nonlinear fitting ability, and its uncertainty predictions can deliver the confidence level of the estimations, which is well adapted to deal with complex industrial processes. However, disturbances and noises in outputs might lead to mispredictions for new samples. In this article, a method using the modified likelihood is proposed to deal with the output corrupted by noises, which aims to achieve a more stable and reliable generative model. Furthermore, the proposed method is applied to a simulation experiment and an actual hydrocracking process to model the relationship between the input variables and the light ends, and the experimental results demonstrate the efficiency of the proposed method.