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Data–driven online modelling for a UGI gasification process using modified lazy learning with a relevance vector machine Cover

Data–driven online modelling for a UGI gasification process using modified lazy learning with a relevance vector machine

International Journal of Applied Mathematics and Computer Science
Volume 31 (2021): Issue 2 (June 2021)
By: Shida Liu,  Honghai Ji,  Zhongsheng Hou,  Jiashuo Zuo and  Lingling Fan  
Open Access
|Jul 2021

Authors

Shida Liu

lsdshiwo@hotmail.com

School of Electrical and Control Engineering, China University of Technology, China

Honghai Ji

jhh@ncut.edu.cn

School of Electrical and Control Engineering, China University of Technology, China

Zhongsheng Hou

zhshhou@bjtu.edu.cn

School of Automation Qingdao University, Qingdao, China

Jiashuo Zuo

Chinazuojiashuo@stumail.ysu.edu.cn

Robot Control Laboratory, School of Electronic Engineering Yanshan University, China

Lingling Fan

linglingfan@bistu.edu.cn

School of Automation Beijing Information Science, Technology University, China
DOI: https://doi.org/10.34768/amcs-2021-0022 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
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Language: English
Page range: 321 - 335
Submitted on: Apr 24, 2020
|
Accepted on: Feb 9, 2021
|
Published on: Jul 8, 2021
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
data-driven modelling,
UGI gasification process,
relevance vector machine,
modified lazy learning
Related subjects:
Mathematics,
Applied mathematics

© 2021 Shida Liu, Honghai Ji, Zhongsheng Hou, Jiashuo Zuo, Lingling Fan, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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