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Ping Zhou 0003
Person information
- affiliation (PhD 2013): Northeastern University, State Key Laboratory of Synthetical Automation for Process Industries, Shenyang, China
Other persons with the same name
- Ping Zhou — disambiguation page
- Ping Zhou 0001
— Zhejiang University, School of Public Affairs, Department of Information Resources Management, Hangzhou, China
- Ping Zhou 0002
- Ping Zhou 0004
- Ping Zhou 0005
- Ping Zhou 0006
- Ping Zhou 0007 — University of Electronic Science and Technology of China, Cancer/Hospital Center, Chengdu, China
- Ping Zhou 0008 — University of California, San Diego, USA
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2020 – today
- 2024
- [j46]Ping Zhou
Guest Editorial Special Issue on Learning From Imperfect Data for Industrial Automation. IEEE Trans Autom. Sci. Eng. 21(2): 1088-1091 (2024) - [j45]Chaoyao Wen, Ping Zhou
Online Sequential Sparse Robust Neural Networks With Random Weights for Imperfect Industrial Streaming Data Modeling. IEEE Trans Autom. Sci. Eng. 21(2): 1163-1175 (2024) - [j44]Ping Zhou
Adaptive Constraint Penalty-Based Multiobjective Operation Optimization of an Industrial Dynamic System With Complex Multiconstraint. IEEE Trans. Cybern. 54(8): 4724-4737 (2024) - [j43]Mingjie Li, Ping Zhou
Objective PDF-Shaping Based Stochastic Optimization With Probabilistic Constraints and Its Application. IEEE Trans. Ind. Electron. 71(3): 3024-3033 (2024) - [j42]Yue Liu
Optimal Tracking Control of Blast Furnace Molten Iron Quality Based on Krotov's Method and Nonlinear Subspace Identification. IEEE Trans. Ind. Electron. 71(8): 9610-9619 (2024) - [j41]Jing Nan
An Interpretable Constructive Algorithm for Incremental Random Weight Neural Networks and Its Application. IEEE Trans. Ind. Informatics 20(12): 13622-13632 (2024) - [j40]Yu Zhang
Anomaly Detection of Industrial Smelting Furnace Incorporated With Accelerated Sampling Denoising Diffusion Probability Model and Conv-Transformer. IEEE Trans. Instrum. Meas. 73: 1-11 (2024) - 2023
- [j39]Ping Zhou, Hongpeng Li
SPEA2 based on grid density search and elite guidance for multi-objective operation optimization of wastewater treatment process. Appl. Soft Comput. 144: 110529 (2023) - [j38]Ping Zhou
Enhanced NMPC for Stochastic Dynamic Systems Driven by Control Error Compensation With Entropy Optimization. IEEE Trans. Control. Syst. Technol. 31(5): 2217-2230 (2023) - [j37]Ping Zhou
Multiobjective Operation Optimization of Wastewater Treatment Process Based on Reinforcement Self-Learning and Knowledge Guidance. IEEE Trans. Cybern. 53(11): 6896-6909 (2023) - [j36]Yong Zhang
Inverse Calculation of Burden Distribution Matrix Using B-Spline Model Based PDF Control in Blast Furnace Burden Charging Process. IEEE Trans. Ind. Informatics 19(1): 317-327 (2023) - [j35]Ruiyao Zhang
Anomaly Detection of Nonstationary Long-Memory Processes Based on Fractional Cointegration Vector Autoregression. IEEE Trans. Reliab. 72(4): 1383-1394 (2023) - [i1]Jing Nan, Wei Dai, Guan Yuan, Ping Zhou:
An Interpretable Constructive Algorithm for Incremental Random Weight Neural Networks and Its Application. CoRR abs/2307.00185 (2023) - 2022
- [j34]Wei Dai
Incremental learning paradigm with privileged information for random vector functional-link networks: IRVFL+. Neural Comput. Appl. 34(9): 6847-6859 (2022) - [j33]Ping Zhou
Kalman Filter-Based Data-Driven Robust Model-Free Adaptive Predictive Control of a Complicated Industrial Process. IEEE Trans Autom. Sci. Eng. 19(2): 788-803 (2022) - [j32]Ping Zhou
Identification of Abnormal Conditions for Fused Magnesium Melting Process Based on Deep Learning and Multisource Information Fusion. IEEE Trans. Ind. Electron. 69(3): 3017-3026 (2022) - [j31]Qianjin Wang
Compact Incremental Random Weight Network for Estimating the Underground Airflow Quantity. IEEE Trans. Ind. Informatics 18(1): 426-436 (2022) - 2021
- [j30]Ping Zhou, Weiqi Chen, Chengming Yi, Zhaohui Jiang, Tao Yang, Tianyou Chai:
Fast just-in-time-learning recursive multi-output LSSVR for quality prediction and control of multivariable dynamic systems. Eng. Appl. Artif. Intell. 100: 104168 (2021) - [j29]Ping Zhou
Data-Driven Monitoring and Diagnosing of Abnormal Furnace Conditions in Blast Furnace Ironmaking: An Integrated PCA-ICA Method. IEEE Trans. Ind. Electron. 68(1): 622-631 (2021) - [j28]Mingjie Li
Data-Driven Multiobjective Predictive Optimal Control of Refining Process With Non-Gaussian Stochastic Distribution Dynamics. IEEE Trans. Ind. Informatics 17(11): 7269-7278 (2021) - [j27]Ping Zhou
Improved Incremental RVFL With Compact Structure and Its Application in Quality Prediction of Blast Furnace. IEEE Trans. Ind. Informatics 17(12): 8324-8334 (2021) - [j26]Ping Zhou
Intelligent Prediction of Train Delay Changes and Propagation Using RVFLNs With Improved Transfer Learning and Ensemble Learning. IEEE Trans. Intell. Transp. Syst. 22(12): 7432-7444 (2021) - 2020
- [j25]Ping Zhou
Recursive Learning-Based Bilinear Subspace Identification for Online Modeling and Predictive Control of a Complicated Industrial Process. IEEE Access 8: 62531-62541 (2020) - [j24]Yuyang Zhou
Dynamic performance enhancement for nonlinear stochastic systems using RBF driven nonlinear compensation with extended Kalman filter. Autom. 112 (2020) - [j23]Jin Xie, Ping Zhou
Robust stochastic configuration network multi-output modeling of molten iron quality in blast furnace ironmaking. Neurocomputing 387: 139-149 (2020) - [j22]Mingjie Li
Data-Driven Predictive Probability Density Function Control of Fiber Length Stochastic Distribution Shaping in Refining Process. IEEE Trans Autom. Sci. Eng. 17(2): 633-645 (2020) - [j21]Hui Zhao
Distributed Robust Event-Triggered Control Strategy for Multiple High-Speed Trains With Communication Delays and Input Constraints. IEEE Trans. Control. Netw. Syst. 7(3): 1453-1464 (2020) - [j20]Ping Zhou
Robust Online Sequential RVFLNs for Data Modeling of Dynamic Time-Varying Systems With Application of an Ironmaking Blast Furnace. IEEE Trans. Cybern. 50(11): 4783-4795 (2020) - [j19]Mingjie Li
Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking. IEEE Trans. Syst. Man Cybern. Syst. 50(3): 1208-1215 (2020)
2010 – 2019
- 2019
- [j18]Ping Zhou
Data modeling for quality prediction using improved orthogonal incremental random vector functional-link networks. Neurocomputing 365: 1-9 (2019) - [j17]Wei Dai
Stochastic configuration networks with block increments for data modeling in process industries. Inf. Sci. 484: 367-386 (2019) - [j16]Mingjie Li, Ping Zhou
Geometric Analysis Based Double Closed-Loop Iterative Learning Control of Output PDF Shaping of Fiber Length Distribution in Refining Process. IEEE Trans. Ind. Electron. 66(9): 7229-7238 (2019) - 2018
- [j15]Ping Zhou
Modeling error PDF optimization based wavelet neural network modeling of dynamic system and its application in blast furnace ironmaking. Neurocomputing 285: 167-175 (2018) - [j14]Ping Zhou
Data-driven predictive control of molten iron quality in blast furnace ironmaking using multi-output LS-SVR based inverse system identification. Neurocomputing 308: 101-110 (2018) - [j13]Yuyang Zhou
EKF-Based Enhanced Performance Controller Design for Nonlinear Stochastic Systems. IEEE Trans. Autom. Control. 63(4): 1155-1162 (2018) - [j12]Ping Zhou
Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking. IEEE Trans. Neural Networks Learn. Syst. 29(9): 4007-4021 (2018) - [c5]Liang Wen, Ping Zhou, Hong Wang
Model Free Adaptive Predictive Control of Multivariate Molten Iron Quality in Blast Furnace Ironmaking. CDC 2018: 2617-2622 - 2017
- [j11]Ping Zhou
Modeling for output fiber length distribution of refining process using wavelet neural networks trained by NSGA II and gradient based two-stage hybrid algorithm. Neurocomputing 238: 24-32 (2017) - [j10]Dongliang Cui
Parametric design-based multi-objective optimisation for high-pressure turbine disc. Int. J. Prod. Res. 55(17): 4847-4861 (2017) - [j9]Ping Zhou
Data-Driven Nonlinear Subspace Modeling for Prediction and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking. IEEE Trans. Control. Syst. Technol. 25(5): 1761-1774 (2017) - [j8]Ping Zhou
Data-Driven Robust RVFLNs Modeling of a Blast Furnace Iron-Making Process Using Cauchy Distribution Weighted M-Estimation. IEEE Trans. Ind. Electron. 64(9): 7141-7151 (2017) - 2015
- [j7]Ping Zhou
Multivariable dynamic modeling for molten iron quality using online sequential random vector functional-link networks with self-feedback connections. Inf. Sci. 325: 237-255 (2015) - 2014
- [j6]Ping Zhou, Shaowen Lu, Tianyou Chai:
Data-Driven Soft-Sensor Modeling for Product Quality Estimation Using Case-Based Reasoning and Fuzzy-Similarity Rough Sets. IEEE Trans Autom. Sci. Eng. 11(4): 992-1003 (2014) - [j5]Shaowen Lu, Ping Zhou, Tianyou Chai, Wei Dai:
Modeling and Simulation of Whole Ball Mill Grinding Plant for Integrated Control. IEEE Trans Autom. Sci. Eng. 11(4): 1004-1019 (2014) - [j4]Ping Zhou, Wei Dai, Tianyou Chai:
Multivariable Disturbance Observer Based Advanced Feedback Control Design and Its Application to a Grinding Circuit. IEEE Trans. Control. Syst. Technol. 22(4): 1474-1485 (2014) - 2013
- [j3]Ping Zhou, Tianyou Chai, Jing Sun
Intelligence-Based Supervisory Control for Optimal Operation of a DCS-Controlled Grinding System. IEEE Trans. Control. Syst. Technol. 21(1): 162-175 (2013) - 2012
- [j2]Ping Zhou, Tianyou Chai, Jin-Hui Zhao:
DOB Design for Nonminimum-Phase Delay Systems and Its Application in Multivariable MPC Control. IEEE Trans. Circuits Syst. II Express Briefs 59-II(8): 525-529 (2012)
2000 – 2009
- 2009
- [j1]Ping Zhou, Tianyou Chai, Hong Wang
Intelligent Optimal-Setting Control for Grinding Circuits of Mineral Processing Process. IEEE Trans Autom. Sci. Eng. 6(4): 730-743 (2009) - [c4]Ping Zhou, Tianyou Chai, Qiang Liu, Hong Wang
Frequency-domain weighted RLS model reduction for complex SISO linear system. ACC 2009: 5719-5724 - 2008
- [c3]Ping Zhou, Tianyou Chai, Hong Wang
Multivariable decoupling internal model control for grinding circuit. ACC 2008: 2475-2480 - 2007
- [c2]Ping Zhou, Jinliang Ding
An intelligent approach for supervisory control of grinding product particle size. CDC 2007: 1856-1861 - 2006
- [c1]Jinliang Ding, Ping Zhou, Changxin Liu, Tianyou Chai:
Hybrid Intelligent System for Supervisory Control of Mineral Grinding Process. ISDA (1) 2006: 979-984
Coauthor Index
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