Dynamic-Inner Partial Least Squares for Dynamic Data Modeling

被引:68
作者
Dong, Yining [1 ]
Qin, S. Joe [2 ]
机构
[1] Univ So Calif, Ming Hsieh Dept Elect Engn, Los Angeles, CA 90089 USA
[2] Univ So Calif, Mork Family Dept Chem Engn & Mat Sci, Los Angeles, CA 90089 USA
来源
IFAC PAPERSONLINE | 2015年 / 48卷 / 08期
关键词
dynamic partial least squares; data-driven modeling; PLS APPROACH; REGRESSION;
D O I
10.1016/j.ifacol.2015.08.167
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Partial least squares(PLS) regression has been widely used to capture the relationship between inputs and outputs in static system modeling. Several dynamic PLS algorithms were proposed to capture the characteristic of dynamic systems. However, none of these algorithms provides an explicit description for dynamic inner model and outer model. In this paper, a dynamic inner PLS is proposed for dynamic system modelling. The proposed algorithm gives explicit dynamic inner model and makes inner model and outer model consistent at the same time. Several examples are given to show the effectiveness of the proposed algorithm. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码:117 / 122
页数:6
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