- [1]Shiwen Xie, Yongfang Xie, Tingwen Huang, Weihua Gui.Multiobjective-based optimization and control for iron removal process under dynamic environment.IEEE Transactions on Industrial Informatics, 2021, 17 (1) : 569-577.
- [2]Shiwen Xie, Yongfang Xie, Hao Ying, Zhaohui Jiang, Weihua Gui.Neurofuzzy-based plant-wide hierarchical coordinating optimization and control: An application to zinc hydrometallurgy plant.IEEE Transactions on Industrial Electronics, 2020, 67 (3) : 2207-2219.
- [3]Shiwen Xie, Yongfang Xie, Fanbiao Li, Chunhua Yang, Weihua Gui.Optimal setting and control for iron removal process based on adaptive neural network soft-sensor.IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 50 (7) : 2408-2420.
- [4]Shiwen Xie, Yongfang Xie, Tingwen Huang, Weihua Gui, Chunhua Yang.Generalized predictive control for industrial process based on neuron adaptive splitting and merging RBF neural network.IEEE Transactions on Industrial Electronics, 2019, 66 (2) : 1192-1202.
- [5]Yongfang Xie, Shiwen Xie, Jinjin Yu, Tingwen Huang, Weihua Gui.On-line prediction of ferrous ion concentration in goethite process based on self-adjusting structure RBF neural network.Neural Networks, 2019, 116: 1-10.
- [6]Mingxi Ai, Shiwen Xie, Yongfang Xie, Jin Zhang, Weihua Gui.Fuzzy association rule-based set-point adaptive optimization and control for the flotation process.Neural Computing & Applications, 2020, 32 (17) : 14019-14029.
- [7]Mingxi Ai, Shiwen Xie, Yongfang Xie, Weihua Gui.Shape-weighted bubble size distribution based reagent predictive control for the antimony flotation process.Chemometrics and Intelligent Laboratory Systems, 2019, 192: 103821.
- [8]Mingxi Ai, Shiwen Xie, Yongfang Xie, Fanbiao Li, Weihua Gui.Data-driven-based adaptive fuzzy neural network control for the antimony flotation plant.Journal of the Franklin Institute, 2019, 356: 5944-5960.
- [9]Shiwen Xie, Fanbiao Li, Yongfang Xie, Zhaohui Jiang, Weihua Gui.Hybrid fuzzy control for the goethite process in zinc production plant combining type-1 and type-2 fuzzy logics.Neurocomputing, 2019, 366 (13) : 170-177.
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