文献类型：期刊文献

英文题名：Robust Sparse Gaussian Process Regression for Soft Sensing in Industrial Big Data Under the Outlier Condition

作者：Huang, Haojie[1,2];Peng, Xin[3];Du, Wei[3];Zhong, Weimin[4,5]

机构：[1]Fuzhou Univ, Coll Elect Engn & Automat, Fuzhou 350116, Peoples R China;[2]Fuzhou Univ, Key Lab Ind Automat Control Technol & Informat Pro, Fuzhou 350116, Peoples R China;[3]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[4]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[5]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai 200237, Peoples R China

年份：2024

卷号：73

起止页码：1

外文期刊名：IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

收录：;EI(收录号：20241115731956);WOS:【SCI-EXPANDED(收录号:WOS:001188560600011)】；

基金：No Statement Available

语种：英文

外文关键词：Kernel; Data models; Complexity theory; Gaussian processes; Soft sensors; Computational modeling; Big Data; Gaussian process regression (GPR); robustness; soft sensor; sparse GPR

摘要：The presence of outliers in the training data affects the accuracy of the constructed model. To cope with the outlier interference in the model construction process, some robust methods have been proposed on the basis of the nonparametric method, Gaussian process regression (GPR), without eliminating the outliers previously. However, the high complexity of these robust GPR methods makes them unable to cope with situations where the amount of data is too large. In this article, we analyze the impact of outliers on model construction in the setting of big data and propose a robust version based on the sparse GPR. Empirical evaluations conducted on two publicly available datasets, as well as a nitrogen oxides soft sensor designed for a physical diesel engine whose data exist outliers that are difficult to distinguish from normal data, provide compelling evidence to support the notion that the proposed method leads to significant enhancements in performance.