Non-linear PLS approach in score surface

被引:22
作者
Kohonen, Jarno [1 ]
Reinikainen, Satu-Pia [1 ]
Aaljoki, Kari [2 ]
Hoskuldsson, Agnar [3 ]
机构
[1] Lappeenranta Univ Technol, Lappeenranta 53851, Finland
[2] Neste Engn, Porvoo 06101, Finland
[3] Ctr Adv Data Anal, DK-2800 Lyngby, Denmark
关键词
Non-linear PLS; Score surface; NIR; Oil refining; PARTIAL LEAST-SQUARES; SENSITIVITY-ANALYSIS; PORTLAND-CEMENT; REGRESSION; MODELS; OPTIMIZATION; SPECTROSCOPY; MANUFACTURE; EXPLORATION; CALIBRATION;
D O I
10.1016/j.chemolab.2009.03.010
中图分类号
TP [自动化技术、计算机技术];
学科分类号
080201 [机械制造及其自动化];
摘要
In empirical modelling, linear models are used the most frequently. This functions well in normal operations. There are situations, however, where it can be seen that the response value behaves in a non-linear manner. In Such a case, it may be futile to attempt the modelling procedure in a linear way. There are several approaches presented for these types of situations and the present work considers the use of powers of score vectors instead of merely using linear terms. The data originates from an oil refinery and Suffers from a mild non-linearity. The data is modelled using PLS, polynomial PLS and non-linear PLS. It can be seen that the non-linear extension of PLS can provide with better predictions at the extreme low and high Values compared to other considered methods. (c) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:159 / 163
页数:5
相关论文
共 31 条
[1]
[Anonymous], 2007, APPL CHEMOMETRICS SC
[2]
Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction [J].
Balabin, Roman M. ;
Safieva, Ravilya Z. ;
Lomakina, Ekaterma I. .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2007, 88 (02) :183-188
[3]
Predictive on-line monitoring of continuous processes [J].
Chen, G ;
McAvoy, TJ .
JOURNAL OF PROCESS CONTROL, 1998, 8 (5-6) :409-420
[4]
Multiblock PLS-based localized process diagnosis [J].
Choi, SW ;
Lee, IB .
JOURNAL OF PROCESS CONTROL, 2005, 15 (03) :295-306
[5]
Evaluation of nonlinear model building strategies for the determination of glucose in biological matrices by near-infrared spectroscopy [J].
Ding, Q ;
Small, GW ;
Arnold, MA .
ANALYTICA CHIMICA ACTA, 1999, 384 (03) :333-343
[6]
Local polynomial additive regression through PLS and splines: PLSS [J].
Durand, JF .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2001, 58 (02) :235-246
[7]
CV-ANOVA for significance testing of PLS and OPLS® models [J].
Eriksson, Lennart ;
Trygg, Johan ;
Wold, Svante .
JOURNAL OF CHEMOMETRICS, 2008, 22 (11-12) :594-600
[8]
Multiple regression for environmental data: nonlinearities and prediction bias [J].
Geladi, P ;
Hadjiiski, L ;
Hopke, P .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1999, 47 (02) :165-173
[9]
A comparison of modeling nonlinear systems with artificial neural networks and partial least squares [J].
Hadjiiski, L ;
Geladi, P ;
Hopke, P .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1999, 49 (01) :91-103
[10]
The Heisenberg modelling procedure and application to nonlinear modelling [J].
Höskuldsson, A .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1998, 44 (1-2) :15-30