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

页码：117 / 122

页数：6

共 50 条

[1] Dynamic Autoregressive Partial Least Squares for Supervised Modeling
Zhu, Qinqin
IFAC PAPERSONLINE, 2021, 54 (07): : 234 - 239
[2] Localized, Adaptive Recursive Partial Least Squares Regression for Dynamic System Modeling
Ni, Wangdong
Tan, Soon Keat
Ng, Wun Jern
Brown, Steven D.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2012, 51 (23) : 8025 - 8039
[3] Dynamic Nonlinear Partial Least Squares Modeling Using Gaussian Process Regression
Liu, Hongbin
Yang, Chong
Carlsson, Bengt
Qin, S. Joe
Yoo, ChangKyoo
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2019, 58 (36) : 16676 - 16686
[4] A dynamic-inner convolutional autoencoder for process monitoring
Zhang, Shuyuan
Qiu, Tong
COMPUTERS & CHEMICAL ENGINEERING, 2022, 158
[5] Gasoline Dry Point Prediction of Fractionation Processes Using Dynamic Inner Partial Least Squares
Xu, Xue
Liu, Qiang
Ding, Jinliang
2017 11TH ASIAN CONTROL CONFERENCE (ASCC), 2017, : 1438 - 1442
[6] Dynamic prediction of multivariate functional data based on Functional Kernel Partial Least Squares
Qian, Qingting
Li, Min
Xu, Jinwu
JOURNAL OF PROCESS CONTROL, 2022, 116 : 273 - 285
[7] Dynamic-Inner White Component Analysis: A New Latent Variable Model for Dynamic Data Analytics and Process Monitoring
Huo, Kaida
Huang, Dexian
Yang, Fan
Shang, Chao
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2025, 22 : 5614 - 5626
[8] Dynamic Process Calibration Based on Sparse Partial Least Squares
Wen, Qiaojun
Ge, Zhiqiang
Song, Zhihuan
Wang, Peiliang
2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA), 2014, : 1366 - 1371
[9] Dynamic transfer partial least squares for domain adaptive regression
Zhao, Zhijun
Yan, Gaowei
Ren, Mifeng
Cheng, Lan
Zhu, Zhujun
Pang, Yusong
JOURNAL OF PROCESS CONTROL, 2022, 118 : 55 - 68
[10] Dynamic-Inner Canonical Correlation and Causality Analysis for High Dimensional Time Series Data
Dong, Yining
Qin, S. Joe
IFAC PAPERSONLINE, 2018, 51 (18): : 476 - 481

← 1 2 3 4 5 →