Data-driven predictive control of molten iron quality in blast furnace ironmaking using multi-output LS-SVR based inverse system identification-Reference-Cited by-同舟云学术

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Data-driven predictive control of molten iron quality in blast furnace ironmaking using multi-output LS-SVR based inverse system identification

Published:2018-09 Issue: Volume:308 Page:101-110
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Zhou Ping^ORCID,Guo Dongwei,Chai Tianyou

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

State (Beijing) Key Laboratory of Process Automation in Mining & Metallurgy

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference28 articles.

1. Data-driven robust RVFLNs modeling of blast furnace ironmaking process using Cauchy distribution weighted M-estimation;Zhou;IEEE Trans. Ind. Electron.,2017

2. Constructing multiple kernel learning framework for blast furnace automation;Jian;IEEE Trans. Autom. Sci. Eng.,2012

3. Multivariable dynamic modeling for molten iron quality using online sequential random vector functional-link networks with self-feedback connections;Zhou;Inf. Sci.,2015

4. Data-driven time discrete models for dynamic prediction of the hot metal silicon in the blast furnace – a review;Saxén;IEEE Trans. Ind. Inf,2013

5. BLASTDAR–a large radar sensor array system for blast furnace burden surface imaging;Zankl;IEEE Sens. J.,2015

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