文献类型：期刊文献

中文题名：基于关键变量的OPLS预测方法

英文题名：OPLS Prediction Method Based on Critical Variables

作者：罗明英[1];王帆[1];谭帅[1];侍洪波[1]

机构：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237

年份：2016

卷号：42

期号：4

起止页码：529

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；

基金：国家自然科学基金(61374140;61403072)

语种：中文

中文关键词：回归模型;关键变量;OPLS算法;质量预测

外文关键词：regression model;critical variable;OPLS method;quality prediction

摘要：产品的最终质量主要是由生产过程中的关键变量决定的,因此,回归模型的质量预测能力与过程变量的选择密切相关。本文提出了一种新的基于关键变量(CV)的OPLS预测方法(CVOPLS),用于输出变量较多过程的质量预测。首先,根据关键变量选取准则,为每个质量变量选取建模所需的关键过程变量。为了减少最后需要建立的模型个数,将由质量变量及其相应的关键过程变量组成的数据阵进行重组,并用OSC算法去除重组后的数据阵中与质量变量无关的干扰信息。然后,对校正后的数据阵建立PLS模型,求取相应的模型回归系数,得到最终的质量预测结果。与传统的PLS及OPLS方法相比,该方法能够在保证模型较好预测精度的前提下,有效地简化模型结构。最后,通过Tennessee Eastman(TE)过程的实验仿真验证了CV-OPLS方法的有效性。
The final quality of product is mainly decided by those critical variables in production process,so the quality prediction ability is closely dependent on the selected process variables.This paper proposes a critical-variable-based OPLS prediction method,CV-OPLS model,for the quality prediction of industrial processes with multi output variables.First,according to the selection criteria of critical variables,we choose critical process variables for each quality variable in modeling.In order to reduce the number of final models,the data matrix composed of quality variable and its critical variables is recombined,in which disturbing variation irrelevant with quality variable will be removed by means of OSC method.And then,PLS models are formed on the corrected data matrix,and the regression coefficients are computed such that the final quality prediction results are obtained.Compared with the traditional PLS and OPLS,the proposed method can effectively simplify model structure and attain superior prediction performance.Finally,the feasibility and effectiveness of the CV-OPLS method are further verified through experiments in Tennessee Eastman（TE）process.