Multi-output Gaussian Process Regression Model with Combined Kernel Function for Polyester Esterification Processes

作     者：王恒骞 耿君先 陈磊 WANG Hengqian;GENG Junxian;CHEN Lei

作者机构：Engineering Research Center of Digitized Textile&Apparel TechnologyMinistry of EducationDonghua UniversityShanghai 201620China College of Information Science and TechnologyDonghua UniversityShanghai 201620China 

出 版 物：《Journal of Donghua University(English Edition)》 (东华大学学报（英文版）)

年 卷 期：2023年第40卷第1期

页      面：27-33页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Natural Science Foundation of Shanghai China(No.19ZR1402300) 

主　　题：seasonal and trend decomposition using loess(STL) multi-output Gaussian process regression combined kernel function polyester esterification process 

摘      要：In polyester fiber industrial processes,the prediction of key performance indicators is vital for product *** esterification process is an indispensable step in the polyester polymerization *** has the characteristics of strong coupling,nonlinearity and complex *** solve these problems,we put forward a multi-output Gaussian process regression(MGPR)model based on the combined kernel function for the polyester esterification *** the seasonal and trend decomposition using loess(STL)can extract the periodic and trend characteristics of time series,a combined kernel function based on the STL and the kernel function analysis is constructed for the *** effectiveness of the proposed model is verified by the actual polyester esterification process data collected from fiber production.