Modeling for output fiber length distribution of refining process using wavelet neural networks trained by NSGA II and gradient based two-stage hybrid algorithm. - Ping Zhou 003; Mingjie Li 001; Dongwei Guo; Hong Wang 001; Tianyou Chai (Neurocomputing, 2017)

Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking. - Ping Zhou 003; Chenyu Wang; Mingjie Li 001; Hong Wang 001; Yongjian Wu; Tianyou Chai (Neurocomputing, 2018)

Geometric Analysis Based Double Closed-Loop Iterative Learning Control of Output PDF Shaping of Fiber Length Distribution in Refining Process. - Mingjie Li 001; Ping Zhou 003; Hong Wang 001; Tianyou Chai (IEEE Trans. Ind. Electron., 2019)

Data-Driven Predictive Probability Density Function Control of Fiber Length Stochastic Distribution Shaping in Refining Process. - Mingjie Li 001; Ping Zhou 003; Yunlong Liu; Hong Wang 001 (IEEE Trans Autom. Sci. Eng., 2020)

Robust Online Sequential RVFLNs for Data Modeling of Dynamic Time-Varying Systems With Application of an Ironmaking Blast Furnace. - Ping Zhou 003; Wenpeng Li; Hong Wang 001; Mingjie Li 001; Tianyou Chai (IEEE Trans. Cybern., 2020)

Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking. - Mingjie Li 001; Ping Zhou 003; Hong Wang 001; Tianyou Chai (IEEE Trans. Syst. Man Cybern. Syst., 2020)

Data-Driven Multiobjective Predictive Optimal Control of Refining Process With Non-Gaussian Stochastic Distribution Dynamics. - Mingjie Li 001; Ping Zhou 003; Liangyong Wang; Ye Yuan 001 (IEEE Trans. Ind. Informatics, 2021)

Objective PDF-Shaping Based Stochastic Optimization With Probabilistic Constraints and Its Application. - Mingjie Li 001; Ping Zhou 003; Hong Wang 001 (IEEE Trans. Ind. Electron., 2024)

Ironmaking process modeling uncertainty quantification via conformal prediction based on random vector functional link networks. - Ping Zhou 003; Chaoyao Wen; Peng Zhao; Mingjie Li 001 (Comput. Electr. Eng., 2025)