Generalized predictive control using improved recurrent fuzzy neural network for a boiler-turbine unit
Tài liệu tham khảo
Bi, 2016, A big data clustering algorithm for mitigating the risk of customer churn, IEEE Trans. Ind. Inform., 12, 1270, 10.1109/TII.2016.2547584
Chao, 2019, An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction, Appl. Soft Comput., 78, 150, 10.1016/j.asoc.2019.02.032
Chavoshian, 2020, Recurrent neuro-fuzzy model of pneumatic artificial muscle position, J. Mech. Sci. Technol., 34, 499, 10.1007/s12206-019-1154-8
Chen, 2010, Clean coal technology development in China, Energy Policy, 38, 2123, 10.1016/j.enpol.2009.06.003
Cheng, 2021, An improved neuro-fuzzy generalized predictive control of ultra-supercritical power plant, Cogn. Comput., 13, 1556, 10.1007/s12559-021-09949-z
Cheng, 2020, Fuzzy K-means cluster based generalized predictive control of ultra supercritical power plant, IEEE Trans. Ind. Inform., 17, 4575, 10.1109/TII.2020.3020259
Draganescu, 2015, Generalized predictive control for superheated steam temperature regulation in a supercritical coal-fired power plant, Csee J. Power Energy Syst., 1, 69, 10.17775/CSEEJPES.2015.00009
Du, 2020, A TSK-type convolutional recurrent fuzzy network for predicting driving fatigue, IEEE Trans. Fuzzy Syst., 29, 2100, 10.1109/TFUZZ.2020.2992856
Escaño, 2009, Neurofuzzy model based predictive control for thermal batch processes, J. Process Control, 19, 1566, 10.1016/j.jprocont.2009.07.016
Espinosa, 2004
Fu, 2017, Real-time optimal control of tracking running for high-speed electric multiple unit, Inform. Sci., 376, 202, 10.1016/j.ins.2016.10.024
Grata, 2020, Deep learning as an alternative to super-resolution imaging in UAV systems
Guo, 2021, Novel computer-aided lung cancer detection based on convolutional neural network-based and feature-based classifiers using metaheuristics, Int. J. Imaging Syst. Technol., 31, 1954, 10.1002/ima.22608
Henao, 2017, Approach in nonintrusive type I load monitoring using subtractive clustering, IEEE Trans. Smart Grid, 8, 812
Huang, 2021, Double iterative learning-based polynomial based-RBFNNs driven by the aid of support vector-based kernel fuzzy clustering and least absolute shrinkage deviations, Fuzzy Sets and Systems
Jia, 2020, Classification of electromyographic hand gesture signals using modified fuzzy C-means clustering and two-step machine learning approach, IEEE Trans. Neural Syst. Rehabil. Eng., 28, 1, 10.1109/TNSRE.2020.2986884
Juang, 2010, A locally recurrent fuzzy neural network with support vector regression for dynamic-system modeling, IEEE Trans. Fuzzy Syst., 18, 261
Kocaarslan, 2006, A fuzzy logic controller application for thermal power plants, Energy Convers. Manage., 47, 442, 10.1016/j.enconman.2005.05.010
Kong, 2016, An effective nonlinear multivariable HMPC for USC power plant incorporating NFN-based modeling, IEEE Trans. Ind. Inform., 12, 555, 10.1109/TII.2016.2520579
Li, 2012, Dynamics of clean coal-fired power generation development in China, Energy Policy, 51, 138, 10.1016/j.enpol.2011.06.012
Lin, 2014, Simplified interval type-2 fuzzy neural networks, IEEE Trans. Neural Netw. Learn. Syst., 25, 959, 10.1109/TNNLS.2013.2284603
Liu, 2006, Neuro-fuzzy generalized predictive control of boiler steam temperature, IEEE Trans. Energy Convers., 21, 900, 10.1109/TEC.2005.853758
Liu, 2013, Modeling of a 1000 MW power plant ultra super-critical boiler system using fuzzy-neural network methods, Energy Convers. Manage., 65, 518, 10.1016/j.enconman.2012.07.028
Liu, 2003, Neurofuzzy network modelling and control of steam pressure in 300MW steam-boiler system, Eng. Appl. Artif. Intell., 16, 431, 10.1016/j.engappai.2003.08.006
Liu, 2015, Brain dynamics in predicting driving fatigue using a recurrent self-evolving fuzzy neural network, IEEE Trans. Neural Netw. Learn. Syst., 27, 1, 10.1109/TNNLS.2014.2375591
Liu, 2020, Nonlinear generalized predictive control of the crystal diameter in CZ-Si crystal growth process based on stacked sparse autoencoder, IEEE Trans. Control Syst. Technol., 28, 1132, 10.1109/TCST.2019.2898975
Lu, 2007, Generalized predictive control using recurrent fuzzy neural networks for industrial processes, J. Process Control, 17, 83, 10.1016/j.jprocont.2006.08.003
Lu, 2011, Predictive control using recurrent neural networks for industrial processes, J. Chin. Inst. Eng., 32, 277, 10.1080/02533839.2009.9671504
Milli, 2022, SubtStream: Online subtractive stream clustering algorithm, Concurr. Comput.: Pract. Exper., 34, 1, 10.1002/cpe.6968
Mingoti, 2006, Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms, European J. Oper. Res., 174, 1742, 10.1016/j.ejor.2005.03.039
Mir, 2020, Employing a Gaussian particle swarm optimization method for tuning multi input multi output-fuzzy system as an integrated controller of a micro-grid with stability analysis, Comput. Intell., 36, 225, 10.1111/coin.12257
Mohamed, 2020, Modeling and control of supercritical and ultra-supercritical power plants: A review, Energies, 13, 1, 10.3390/en13112935
Moon, 2003, A boiler-turbine system control using a fuzzy auto-regressive moving average (FARMA) model, IEEE Trans. Energy Convers., 18, 142, 10.1109/TEC.2002.808408
Navid Razmjooy, 2019
Pawlowski, 2013, Generalized predictive control with actuator deadband for event-based approaches, IEEE Trans. Ind. Inform., 10, 523, 10.1109/TII.2013.2270570
Qazani, 2022, An optimal washout filter for motion platform using neural network and fuzzy logic, Eng. Appl. Artif. Intell., 108, 10.1016/j.engappai.2021.104564
Rezaeian, 2017, Generator coherency and network partitioning for dynamic equivalencing using subtractive clustering algorithm, IEEE Syst. J., 12, 3085, 10.1109/JSYST.2017.2665701
Romero, 2015, Low speed hybrid generalized predictive control of a gasoline-propelled car, ISA Trans., 57, 373, 10.1016/j.isatra.2015.01.004
Schwedersky, 2022, Nonlinear model predictive control algorithm with iterative nonlinear prediction and linearization for long short-term memory network models, Eng. Appl. Artif. Intell., 115, 10.1016/j.engappai.2022.105247
Shiwen, 2019, Generalized predictive control for industrial processes based on neuron adaptive splitting and merging RBF neural network, IEEE Trans. Ind. Electron., 66, 1192, 10.1109/TIE.2018.2835402
Silva, 2000, Adaptive regulation of super-heated steam temperature: a case study in an industrial boiler, Control Eng. Pract., 8, 1405, 10.1016/S0967-0661(00)00069-1
Tan, 2004, Tuning of PID controllers for boiler-turbine units, ISA Trans., 43, 571, 10.1016/S0019-0578(07)60169-4
Tan, 2019, Characteristic model–based generalized predictive control and its application to the parafoil and payload system, Optim. Control Appl. Methods, 40, 659, 10.1002/oca.2506
Tian, 2017, Generalized predictive PID control for main steam temperature based on improved PSO algorithm, J. Adv. Comput. Intell. Intell. Inform., 16, 431
Umair, 2022, A network intrusion detection system using hybrid multilayer deep learning model, Big Data, 1
Wang, 2017, Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode, Cluster Comput., 22, 1
Wang, 2019, Efficiency extreme point tracking strategy based on FFRLS online identification for PEMFC system, IEEE Trans. Energy Convers., 34, 952, 10.1109/TEC.2018.2872861
Wang, 2021, Interval type-2 fuzzy neural network based constrained GPC for NH3 flow in SCR de-NOx process, Neural Comput. Appl., 33, 16057, 10.1007/s00521-021-06227-9
Wang, 2022, Stochastic configuration network based cascade generalized predictive control of main steam temperature in power plants, Inform. Sci., 587, 123, 10.1016/j.ins.2021.12.006
Wen, 2020, Long term electric load forecasting based on TS-type recurrent fuzzy neural network model, Electr. Power Syst. Res., 179, 10.1016/j.epsr.2019.106106
Zhang, 2013
Zhang, 2012, Cascade control of superheated steam temperature with neuro-PID controller, ISA Trans., 51, 778, 10.1016/j.isatra.2012.06.008
Zhang, 2013, Generalized predictive control applied in waste heat recovery power plants, Appl. Energy, 102, 320, 10.1016/j.apenergy.2012.07.038