Machine learning based decision making for time varying systems: Parameter estimation and performance optimization
Tài liệu tham khảo
Chai, 2013, Application of decision-making techniques in supplier selection: A systematic review of literature, Expert Syst. Appl., 40, 3872, 10.1016/j.eswa.2012.12.040
Kim, 2008, A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents, Decis. Support Syst., 44, 544, 10.1016/j.dss.2007.07.001
Wang, 2009, Review on multi-criteria decision analysis aid in sustainable energy decision-making, Renew. Sustain. Energy Rev., 13, 2263, 10.1016/j.rser.2009.06.021
Biel, 2016, Systematic literature review of decision support models for energy-efficient production planning, Comput. Ind. Eng., 101, 243, 10.1016/j.cie.2016.08.021
Ibarra-Rojas, 2015, Planning, operation, and control of bus transport systems: A literature review, Transp. Res. B, 77, 38, 10.1016/j.trb.2015.03.002
Ho, 2010, Multi-criteria decision making approaches for supplier evaluation and selection: A literature review, European J. Oper. Res., 202, 16, 10.1016/j.ejor.2009.05.009
Xiong, 2014, Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting, Knowl.-Based Syst., 55, 87, 10.1016/j.knosys.2013.10.012
Boran, 2009, A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method, Expert Syst. Appl., 36, 11363, 10.1016/j.eswa.2009.03.039
Yue, 2011, An extended TOPSIS for determining weights of decision makers with interval numbers, Knowl.-Based Syst., 24, 146, 10.1016/j.knosys.2010.07.014
Rodriguez, 2012, Hesitant fuzzy linguistic term sets for decision making, IEEE Trans. Fuzzy Syst., 20, 109, 10.1109/TFUZZ.2011.2170076
Kenne, 2012, Production planning of a hybrid manufacturing-remanufacturing system under uncertainty within a closed-loop supply chain, Int. J. Prod. Econ., 135, 81, 10.1016/j.ijpe.2010.10.026
Chen, 2013, Interval-valued hesitant preference relations and their applications to group decision making, Knowl.-Based Syst., 37, 528, 10.1016/j.knosys.2012.09.009
LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539
Hastie, 2001
A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, in: 2012 International Conference on Neural Information Processing Systems, NIPS, Nevada, US, 2012, pp. 1097–1105.
T. Mikolov, I. Sutskever, K. Chen, G.S. Corrado, J. Dean, Distributed representations of words and phrases and their compositionality, in: 2013 International Conference on Neural Information Processing Systems, NIPS, Nevada, US, 2013, pp. 3111–3119.
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, A.M. Lopez, The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2016, pp. 3234–3243.
Blum, 1997, Selection of relevant features and examples in machine learning, Artificial Intelligence, 97, 245, 10.1016/S0004-3702(97)00063-5
Huang, 2015, Trends in extreme learning machines: A review, Neural Netw., 61, 32, 10.1016/j.neunet.2014.10.001
Schmidhuber, 2015, Deep learning in neural networks: An overview, Neural Netw., 61, 85, 10.1016/j.neunet.2014.09.003
Guo, 2016, Deep learning for visual understanding: A review, Neurocomputing, 187, 27, 10.1016/j.neucom.2015.09.116
Vaerenbergh, 2012, Kernel recursive least-squares tracker for time-varying regression, IEEE Trans. Neural Netw. Learn. Syst., 23, 1313, 10.1109/TNNLS.2012.2200500
Dangl, 2012, Predictive regressions with time-varying coefficients, J. Financ. Econ., 106, 157, 10.1016/j.jfineco.2012.04.003
Zhang, 2015, Time-varying nonlinear regression models: Nonparametric estimation and model selection, Ann. Statist., 43, 741, 10.1214/14-AOS1299
Garcia, 1989, Model predictive control: Theory and practice-A survey, Automatica, 25, 335, 10.1016/0005-1098(89)90002-2
Parisio, 2014, A model predictive control approach to microgrid operation optimization, IEEE Trans. Control Syst. Technol., 22, 1813, 10.1109/TCST.2013.2295737
Angeli, 2012, On average performance and stability of economic model predictive control, IEEE Trans. Automat. Control, 57, 1615, 10.1109/TAC.2011.2179349
Kamal, 2013, Model predictive control of vehicles on urban roads for improved fuel economy, IEEE Trans. Control Syst. Technol., 21, 831, 10.1109/TCST.2012.2198478
Froisy, 1994, Model predictive control: Past, present and future, ISA Trans., 33, 235, 10.1016/0019-0578(94)90095-7
Qin, 2003, A survey of industrial model predictive control technology, Control Eng. Pract., 11, 733, 10.1016/S0967-0661(02)00186-7
Mayne, 2014, Model predictive control: Recent developments and future promise, Automatica, 50, 2967, 10.1016/j.automatica.2014.10.128
Zhang, 2017, Data-driven optimal consensus control for discrete-time multi-agent systems with unknown dynamics using reinforcement learning method, IEEE Trans. Ind. Electron., 64, 4091, 10.1109/TIE.2016.2542134
Fadlullah, 2017, State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems, IEEE Commun. Surv. Tutor., 19, 2432, 10.1109/COMST.2017.2707140
Li, 2018, An adaptive SOM neural network method for distributed formation control of a group of AUVs, IEEE Trans. Ind. Electron., 65, 8260
Davis, 2017, Shared decision-making using personal health record technology: a scoping review at the crossroads, J. Amer. Med. Inf. Assoc., 24, 857, 10.1093/jamia/ocw172
Cawley, 2010, On over-fitting in model selection and subsequent selection bias in performance evaluation, J. Mach. Learn. Res., 11, 2079