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

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