Quality prediction via semisupervised Bayesian regression with application to propylene polymerization-Reference-Cited by-同舟云学术

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Quality prediction via semisupervised Bayesian regression with application to propylene polymerization

Published:2018-06-28 Issue:10 Volume:32 Page:e3052
ISSN:0886-9383
Container-title:Journal of Chemometrics
language:en
Short-container-title:Journal of Chemometrics

Author:

Sun Yuanmeng¹^ORCID,Liu Xinggao¹,Zhang Zeyin²

Affiliation:

1. State Key Laboratory of Industry Control Technology, College of Control Science & Engineering; Zhejiang University; Hangzhou 310027 China

2. Department of Mathematics; Zhejiang University; Hangzhou 310027 China

Funder

Aerospace Science and Technology Innovation Fund of China Aerospace Science and Technology Corporation

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

ShanghaiShanghai Aerospace Science and Technology Innovation Fund

China Aerospace Science and Technology Corporation

Publisher

Wiley

Subject

Applied Mathematics,Analytical Chemistry

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/cem.3052/fullpdf

Reference41 articles.

1. Development of particle swarm optimization-support vector regression (PSO-SVR) coupled with microwave plasma torch-atomic emission spectrometry for quality control of ginsengs;Ying;J Chemometr,2017

2. Data-driven soft sensor approach for online quality prediction using state dependent parameter models;Bidar;Chemom Intel Lab Syst,2017

3. Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models;Shao;Chem Eng Res Des,2015

4. Multivariate quality control solved by one-class partial least squares regression: Identification of adulterated peanut oils by mid-infrared spectroscopy;Xu;J Chemometr,2011

5. Offline modeling for product quality prediction of mineral processing using modeling error PDF shaping and entropy minimization;Ding;IEEE Trans Neural Netw,2011

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