Locally Weighted Kernel Principal Component Regression Model for Soft Sensing of Nonlinear Time-Variant Processes-Reference-Cited by-同舟云学术

Locally Weighted Kernel Principal Component Regression Model for Soft Sensing of Nonlinear Time-Variant Processes

Published:2014-08-21 Issue:35 Volume:53 Page:13736-13749
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Yuan Xiaofeng¹,Ge Zhiqiang¹,Song Zhihuan¹

Affiliation:

1. State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Department of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, P. R. China

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/ie4041252

Reference41 articles.

1. Review of Recent Research on Data-Based Process Monitoring

2. Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods

3. Soft Chemical Analyzer Development Using Adaptive Least-Squares Support Vector Regression with Selective Pruning and Variable Moving Window Size

4. Design of inferential sensors in the process industry: A review of Bayesian methods

5. Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method

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