Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants-Reference-Cited by-同舟云学术

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Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants

Published:2014-10 Issue: Volume:137 Page:57-66
ISSN:0169-7439
Container-title:Chemometrics and Intelligent Laboratory Systems
language:en
Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Kaneko Hiromasa,Funatsu Kimito

Funder

Japan Science and Technology Agency

Japan Society for the Promotion of Science

Grant-in-Aid for Young Scientists

Mitsui Chemicals, Inc.

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

Reference31 articles.

1. Data-based process monitoring, process control, and quality improvement: recent developments and applications in steel industry;Kano;Comput. Chem. Eng.,2008

2. Data-driven soft sensors in the process industry;Kadlec;Comput. Chem. Eng.,2009

3. Virtual sensing technology in process industries: trends and challenges revealed by recent industrial applications;Kano;J. Chem. Eng. Jpn,2013

4. Classification of the degradation of soft sensor models and discussion on adaptive models;Kaneko;AICHE J.,2013

5. Review of adaptation mechanisms for data-driven soft sensors;Kadlec;Comput. Chem. Eng.,2011

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