中南大学 Xiaofang Chen

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[1]Constrained voting extreme learning machine and its application[J].JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS, 2021, 32 (1) : 209-219.
[2]A mechanism knowledge-driven method for identifying the pseudo dissolution hysteresis coefficient in the industrial aluminium electrolysis process[J].CONTROL ENGINEERING PRACTICE, 2020, 102
[3]An improved cell situation identification approach with convolutional neural network and wavelet extreme learning machine[J].PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, 2020
[4]Adaptive over-sampling method for classification with application to imbalanced datasets in aluminum electrolysis[J].NEURAL COMPUTING & APPLICATIONS, 2020, 32 (11) : 7183-7199.
[5]Semi-Supervised Ensemble Classification Method Based on Near Neighbor and Its Application[J].PROCESSES, 2020, 8 (4)
[6]A semi-supervised Laplacian extreme learning machine and feature fusion with CNN for industrial superheat identification[J].NEUROCOMPUTING, 2020, 381: 186-195.
[7]Evaluation Strategy And Mass Balance for Making Decision About the Amount of Aluminum Fluoride Addition Based on Superheat Degree[J].JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION, 2020, 16 (2) : 601-622.
[8]Dynamic uncertain causality graph based on cloud model theory for knowledge representation and reasoning[J].INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2020, 11 (8) : 1781-1799.
[9]知识驱动的流程工业智能制造[J].中国科学:信息科学, 2020, 50 (9) : 1345-1360.
[10]A novel shapelet transformation method for classification of multivariate time series with dynamic discriminative subsequence and application in anode current signals[J].Journal of Central South University, 2020, 27 (1) : 114-131.
[11]A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps[J].ENGINEERING, 2019, 5 (6) : 1060-1076.
[12]Knowledge representation and reasoning using self-learning interval type-2 fuzzy Petri nets and extended TOPSIS[J].INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2019, 10 (12) : 3499-3520.
[13]Nonlinear process monitoring using kernel dictionary learning with application to aluminum electrolysis process[J].CONTROL ENGINEERING PRACTICE, 2019, 89: 94-102.
[14]ANODE EFFECT PREDICTION BASED ON COLLABORATIVE TWO-DIMENSIONAL FORECAST MODEL IN ALUMINUM ELECTROLYSIS PRODUCTION[J].JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION, 2019, 15 (2) : 595-618.
[15]Hessian Regularization Semi-supervised Extreme Learning Machine for Superheat Identification in Aluminum Reduction Cell[S].PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019), 2019: 4406-4411.
[16]A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps[J].Engineering, 2019, 5 (6) : 1060-1076+1176-1193.
[17]A Hybrid Regularization Semi-Supervised Extreme Learning Machine Method and Its Application[J].IEEE ACCESS, 2019, 7: 30102-30111.
[18]Heterogeneous cooperative belief for social dilemma in multi-agent system[J].APPLIED MATHEMATICS AND COMPUTATION, 2018, 320: 572-579.
[19]An adaptive time series representation method for anode current signals in aluminium electrolysis[J].IFAC PAPERSONLINE, 2018, 51 (21) : 213-218.
[20]基于主元导数特征聚类的加氢裂化动态调整区间识别[J].清华大学学报(自然科学版), 2018, 58 (1) : 81-86.

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