From Shallow to Deep:A Novel Correlation Network Representation Regression Framework for Modeling and Monitoring MIQ-Driven Blast Furnace Ironmaking ProcessesOA
From Shallow to Deep:A Novel Correlation Network Representation Regression Framework for Modeling and Monitoring MIQ-Driven Blast Furnace Ironmaking Processes
Siwei Lou;Chunjie Yang;Zhe Liu;Hanwen Zhang;Chao Liu;Ping Wu
State Key Laboratory of Industrial Control Technology(SKLICT),Zhejiang University,Hangzhou 310027,ChinaState Key Laboratory of Industrial Control Technology(SKLICT),Zhejiang University,Hangzhou 310027,ChinaState Key Laboratory of Industrial Control Technology(SKLICT),Zhejiang University,Hangzhou 310027,China||State Key Laboratory of Metallurgical Intelligent Manufacturing System,Beijing 100071,ChinaSchool of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,ChinaGuangxi Liuzhou Iron and Steel Group Co.,Ltd.,Liuzhou545002,China 5School of Information Science and Engineering,Zhejiang Sci-Tech University,Hangzhou 310018,China
Canonical correlation analysis(CCA)ironmaking process(IP)molten iron quality(MIQ)neural network(NN)pro-cess monitoring
Canonical correlation analysis(CCA)ironmaking process(IP)molten iron quality(MIQ)neural network(NN)pro-cess monitoring
《自动化学报（英文版）》 2026 (2)
281-299,19
This work was supported in part by the Pioneer Research and Development Program of Zhejiang(2025C01021),Zhejiang Province Postdoctoral Research Project Selection Fund(ZJ2025061),the National Science and Technology Major Project-Intelligent Manufacturing Systems and Robotics of China(2025ZD1602000,2025ZD1601800),the National Natural Science Foundation of China(61933015,62273030,62573387),the Natural Science Foundation of Zhejiang province,China(LY24F030004),and the Fundamental Research Funds of Zhejiang Sci-Tech University(25222139-Y).
评论