Soft sensor for nonlinear processes based on ensemble partial least squares with adaptive localization

The 27th Chinese Control and Decision Conference (2015 CCDC) ◽

10.1109/ccdc.2015.7162017 ◽

2015 ◽

Author(s):

Weiming Shao ◽

Xuemin Tian

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Soft Sensor ◽

Nonlinear Processes

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Soft Sensor Modeling for Unobserved Multimode Nonlinear Processes Based on Modified Kernel Partial Least Squares With Latent Factor Clustering

IEEE Access ◽

10.1109/access.2020.2974783 ◽

2020 ◽

Vol 8 ◽

pp. 35864-35872 ◽

Author(s):

Xiaogang Deng ◽

Yongxuan Chen ◽

Ping Wang ◽

Yuping Cao

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Soft Sensor ◽

Nonlinear Processes ◽

Latent Factor ◽

Kernel Partial Least Squares ◽

Sensor Modeling

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Combining Kernel Partial Least-Squares Modeling and Iterative Learning Control for the Batch-to-Batch Optimization of Constrained Nonlinear Processes

Industrial & Engineering Chemistry Research ◽

10.1021/ie1004702 ◽

2010 ◽

Vol 49 (16) ◽

pp. 7470-7477 ◽

Author(s):

Yingwei Zhang ◽

Yunpeng Fan ◽

Pengchao Zhang

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Iterative Learning Control ◽

Learning Control ◽

Iterative Learning ◽

Nonlinear Processes ◽

Kernel Partial Least Squares

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An adaptive soft sensor based on multi-state partial least squares regression

2015 34th Chinese Control Conference (CCC) ◽

10.1109/chicc.2015.7259921 ◽

2015 ◽

Author(s):

Guo Wei ◽

Pan Tianhong

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Soft Sensor ◽

Least Squares Regression

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Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes

Computers & Chemical Engineering ◽

10.1016/j.compchemeng.2014.07.014 ◽

2014 ◽

Vol 71 ◽

pp. 77-93 ◽

Author(s):

Huaiping Jin ◽

Xiangguang Chen ◽

Jianwen Yang ◽

Lei Wu

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Soft Sensor ◽

Batch Processes ◽

Just In Time ◽

Least Squares Regression ◽

Modeling Framework ◽

Kernel Partial Least Squares ◽

Sensor Modeling

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Co-training partial least squares model for semi-supervised soft sensor development

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2015.08.002 ◽

2015 ◽

Vol 147 ◽

pp. 75-85 ◽

Author(s):

Liang Bao ◽

Xiaofeng Yuan ◽

Zhiqiang Ge

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Soft Sensor ◽

Sensor Development ◽

Partial Least Squares Model

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Highly-overlapped, recursive partial least squares soft sensor with state partitioning via local variable selection

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2018.02.006 ◽

2018 ◽

Vol 175 ◽

pp. 104-115 ◽

Author(s):

Dominic V. Poerio ◽

Steven D. Brown

Keyword(s):

Variable Selection ◽

Least Squares ◽

Partial Least Squares ◽

Soft Sensor ◽

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Online soft sensor design using local partial least squares models with adaptive process state partition

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2015.04.003 ◽

2015 ◽

Vol 144 ◽

pp. 108-121 ◽

Author(s):

Weiming Shao ◽

Xuemin Tian ◽

Ping Wang ◽

Xiaogang Deng ◽

Sheng Chen

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Soft Sensor ◽

Sensor Design ◽

Adaptive Process

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Developing a soft sensor based on sparse partial least squares with variable selection

Journal of Process Control ◽

10.1016/j.jprocont.2014.05.014 ◽

2014 ◽

Vol 24 (7) ◽

pp. 1046-1056 ◽

Author(s):

Jialin Liu

Keyword(s):

Variable Selection ◽

Least Squares ◽

Partial Least Squares ◽

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Robust Online Monitoring Based on Spherical-Kernel Partial Least Squares for Nonlinear Processes with Contaminated Modeling Data

Industrial & Engineering Chemistry Research ◽

10.1021/ie4008776 ◽

2013 ◽

Vol 52 (26) ◽

pp. 9155-9164 ◽

Author(s):

Yi Hu ◽

Hehe Ma ◽

Hongbo Shi

Keyword(s):

Least Squares ◽

Partial Least Squares ◽

Online Monitoring ◽

Nonlinear Processes ◽

Kernel Partial Least Squares ◽

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Enhanced fault detection for nonlinear processes using modified kernel partial least squares and the statistical local approach

The Canadian Journal of Chemical Engineering ◽

10.1002/cjce.23058 ◽

2017 ◽

Vol 96 (5) ◽

pp. 1116-1126 ◽

Author(s):

Li Wang

Keyword(s):

Fault Detection ◽

Least Squares ◽

Partial Least Squares ◽

Nonlinear Processes ◽

Local Approach ◽

Kernel Partial Least Squares

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