文献类型：期刊文献

英文题名：A Sparse Nonstationary Trigonometric Gaussian Process Regression and Its Application on Nitrogen Oxide Prediction of the Diesel Engine

作者：Huang, Haojie[1];Song, Yedong[2];Peng, Xin[1];Ding, Steven X.[3];Zhong, Weimin[1];Du, Wei[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Weichai Power Co Ltd, Shanghai 200000, Peoples R China;[3]Univ Duisburg Essen, Inst Automat Control & Complex Syst, D-47057 Duisburg, Germany

年份：2021

卷号：17

期号：12

起止页码：8367

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20211310151026);WOS:【SCI-EXPANDED(收录号:WOS:000690940600048)】；

基金：This work was supported in part by the National Natural Science Foundation of China (Basic Science Center Program) under Grant 61988101, Grant 61925305, and Grant 61890930-3 and in part by the Optimal Short-term Operation of Petroleum Refineries International (Regional) Cooperation and Exchange Project under Grant 61720106008. Paper no. TII-204752.

语种：英文

外文关键词：Kernel; Gaussian processes; Informatics; Computational complexity; Standards; Sparse representation; Diesel engines; Gaussian process regression (GPR); nonstationarity; sparse Gaussian process regression

摘要：Gaussian process regression (GPR) has shown superiority in terms of state estimation for its nonparametric characteristic and uncertainty prediction ability. Due to its heavy computational complexity, GPR is generally used for small datasets. To efficiently deal with the big data, the sparse spectrum approximation method has been successfully applied to GPR to decrease the computational complexity. However, the stationarity of this method is a strict assumption for data and usually mismatches the industrial processes. In this article, we proposed a sparse nonstationary GPR, which can deal with the nonstationary relationship among samples and make the model more flexible, to settle the aforementioned problems. Furthermore, the performance of the proposed method is evaluated using three public datasets and a sampled diesel engine dataset, and the results show the superiority of our proposed method in terms of accuracy.