Mixture semisupervised principal component regression model and soft sensor application-Reference-Cited by-同舟云学术

Mixture semisupervised principal component regression model and soft sensor application

Published:2013-11-09 Issue:2 Volume:60 Page:533-545
ISSN:0001-1541
Container-title:AIChE Journal
language:en
Short-container-title:AIChE J.

Author:

Ge Zhiqiang¹²,Huang Biao²,Song Zhihuan¹

Affiliation:

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

2. Dept. of Chemical and Materials Engineering; University of Alberta; Edmonton AB T6G2G6 Canada

Funder

National Science Engineering Research Council of Canada (NSERC)

Alberta Innovates Technology Futures (AITF)

National Natural Science Foundation of China (NSFC)

National Project 973

Fundamental Research Funds for the Central Universities

Publisher

Wiley

Subject

General Chemical Engineering,Environmental Engineering,Biotechnology

Reference33 articles.

1. Multivariate concentration determination using principal component regression with residual analysis;Keithley;Trends Anal Chem.,2009

2. Mixture probabilistic PCR model for soft sensing of multimode processes;Ge;Chemom Intell Lab Syst.,2011

3. Improved principal component regression for face recognition under illumination variations;Huang;IEEE Signal Process Lett.,2012

4. Multivariate statistical process monitors;Kruger;US Patent,,2006

5. Complex process monitoring using modified partial least squares method of independent component regression;Zhang;Chemom Intell Lab Syst.,2009

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