Output space transfer based multi-input multi-output Takagi–Sugeno fuzzy modeling for estimation of molten iron quality in blast furnace

Knowledge-Based Systems ◽

10.1016/j.knosys.2021.106906 ◽

2021 ◽

Vol 219 ◽

pp. 106906

Author(s):

Junpeng Li ◽

Changchun Hua ◽

Yana Yang ◽

Limin Zhang ◽

Xinping Guan

Keyword(s):

Blast Furnace ◽

Fuzzy Modeling ◽

Molten Iron ◽

Takagi Sugeno ◽

Output Space ◽

Molten Iron Quality

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Low-rank based Multi-Input Multi-Output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace

Fuzzy Sets and Systems ◽

10.1016/j.fss.2020.08.012 ◽

2020 ◽

Author(s):

Junpeng Li ◽

Changchun Hua ◽

Junlei Qian ◽

Xinping Guan

Keyword(s):

Blast Furnace ◽

Fuzzy Modeling ◽

Molten Iron ◽

Low Rank ◽

Takagi Sugeno ◽

Molten Iron Quality

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A Novel MIMO T-S Fuzzy Modeling for Prediction of Blast Furnace Molten Iron Quality with Missing Outputs

IEEE Transactions on Fuzzy Systems ◽

10.1109/tfuzz.2020.2983667 ◽

2020 ◽

pp. 1-1

Author(s):

Junpeng Li ◽

Changchun Hua ◽

Yana Yang ◽

Xinping Guan

Keyword(s):

Blast Furnace ◽

Fuzzy Modeling ◽

Molten Iron ◽

Molten Iron Quality

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Output Space Transfer-Based MIMO RVFLNs Modeling for Estimation of Blast Furnace Molten Iron Quality With Missing Indexes

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2021.3063200 ◽

2021 ◽

Vol 70 ◽

pp. 1-10

Author(s):

Junpeng Li ◽

Changchun Hua ◽

Yana Yang

Keyword(s):

Blast Furnace ◽

Molten Iron ◽

Output Space ◽

Molten Iron Quality

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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

Neurocomputing ◽

10.1016/j.neucom.2018.04.060 ◽

2018 ◽

Vol 308 ◽

pp. 101-110 ◽

Cited By ~ 7

Author(s):

Ping Zhou ◽

Dongwei Guo ◽

Tianyou Chai

Keyword(s):

Blast Furnace ◽

System Identification ◽

Predictive Control ◽

Molten Iron ◽

Inverse System ◽

Data Driven ◽

Blast Furnace Ironmaking ◽

Molten Iron Quality

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Data-Driven Bayesian-Based Takagi-Sugeno Fuzzy Modeling for Dynamic Prediction of Hot Metal Silicon Content in Blast Furnace

IEEE Transactions on Systems Man and Cybernetics Systems ◽

10.1109/tsmc.2020.3013972 ◽

2020 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Junpeng Li ◽

Changchun Hua ◽

Yana Yang ◽

Xinping Guan

Keyword(s):

Blast Furnace ◽

Silicon Content ◽

Fuzzy Modeling ◽

Data Driven ◽

Hot Metal ◽

Dynamic Prediction ◽

Takagi Sugeno

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Model Free Adaptive Predictive Control of Multivariate Molten Iron Quality in Blast Furnace Ironmaking

2018 IEEE Conference on Decision and Control (CDC) ◽

10.1109/cdc.2018.8619757 ◽

2018 ◽

Author(s):

Liang Wen ◽

Ping Zhou ◽

Hong Wang ◽

Tianyou Chai

Keyword(s):

Blast Furnace ◽

Predictive Control ◽

Molten Iron ◽

Model Free ◽

Blast Furnace Ironmaking ◽

Adaptive Predictive Control ◽

Molten Iron Quality

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Intelligent multivariable modeling of blast furnace molten iron quality based on dynamic AGA-ANN and PCA

Journal of Iron and Steel Research International ◽

10.1016/s1006-706x(15)30031-5 ◽

2015 ◽

Vol 22 (6) ◽

pp. 487-495 ◽

Cited By ~ 13

Author(s):

Meng Yuan ◽

Ping Zhou ◽

Ming-liang Li ◽

Rui-feng Li ◽

Hong Wang ◽

...

Keyword(s):

Blast Furnace ◽

Molten Iron ◽

Molten Iron Quality

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Data-driven recursive subspace identification based online modelling for prediction and control of molten iron quality in blast furnace ironmaking

IET Control Theory and Applications ◽

10.1049/iet-cta.2016.1474 ◽

2017 ◽

Vol 11 (14) ◽

pp. 2343-2351 ◽

Cited By ~ 11

Author(s):

Ping Zhou ◽

Peng Dai ◽

Heda Song ◽

Tianyou Chai

Keyword(s):

Blast Furnace ◽

Molten Iron ◽

Data Driven ◽

Subspace Identification ◽

Blast Furnace Ironmaking ◽

Prediction And Control ◽

And Control ◽

Molten Iron Quality

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A Novel MIMO RVFLNs for Predicting Molten Iron Quality Indexes in Blast Furnace

IEEE Transactions on Industrial Electronics ◽

10.1109/tie.2020.3031525 ◽

2020 ◽

pp. 1-1

Author(s):

Junpeng Li ◽

Changchun Hua ◽

Yana Yang

Keyword(s):

Blast Furnace ◽

Molten Iron ◽

Quality Indexes ◽

Molten Iron Quality

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Data-Driven Nonlinear Subspace Modeling for Prediction and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

IEEE Transactions on Control Systems Technology ◽

10.1109/tcst.2016.2631124 ◽

2017 ◽

Vol 25 (5) ◽

pp. 1761-1774 ◽

Cited By ~ 31

Author(s):

Ping Zhou ◽

Heda Song ◽

Hong Wang ◽

Tianyou Chai

Keyword(s):

Blast Furnace ◽

Molten Iron ◽

Data Driven ◽

Quality Indices ◽

Subspace Modeling ◽

Blast Furnace Ironmaking ◽

Prediction And Control ◽

And Control ◽

Molten Iron Quality

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