Dynamic inferential estimation using principal components regression (PCR)

被引：35

作者：

Hartnett, MK ^{[1
]}

Lightbody, G ^{[1
]}

Irwin, GW ^{[1
]}

机构：

[1] Queens Univ Belfast, Dept Elect & Elect Engn, Control Engn Res Grp, Belfast BT7 1NN, Antrim, North Ireland

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 1998年 / 40卷 / 02期

基金：

英国工程与自然科学研究理事会;

关键词：

inferential estimation; subset selection; genetic algorithms; state-space modelling; Principal Variables method;

D O I：

10.1016/S0169-7439(98)00021-5

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Principal components regression (PCR) is applied to the dynamic inferential estimation of plant outputs from highly correlated data. A genetic algorithm (GA) approach is developed for the optimal selection of subsets from the available measurement variables, thereby providing a method of identifying nonessential elements. The theoretical link between principal components analysis (PCA) and state-space modelling is employed to identify a measurement equation involving the GA-selected subset, which is then used for inferential estimation of the omitted variables. These techniques are successfully demonstrated for the inferential estimation of outputs from a validated industrial benchmark simulation of an overheads condenser and reflux drum model (OCRD). (C) 1998 Elsevier Science B.V. All rights reserved.

引用

页码：215 / 224

页数：10

共 19 条

[1] MULTIVARIATE SELECTION OF VARIABLES IN INDUSTRIAL QUALITY-CONTROL - OPTIMIZING AVIATION FUEL FINAL CONTROL [J].