| A comprehensive survey of multiagent reinforcement learning L Busoniu, R Babuska, B De Schutter IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and …, 2008 | 2733 | 2008 |
| Reinforcement learning and dynamic programming using function approximators L Busoniu, R Babuska, B De Schutter, D Ernst CRC press, 2017 | 1247 | 2017 |
| Equivalence of hybrid dynamical models WPMH Heemels, B De Schutter, A Bemporad Automatica 37 (7), 1085-1091, 2001 | 967 | 2001 |
| Multi-agent reinforcement learning: An overview L Buşoniu, R Babuška, B De Schutter Innovations in multi-agent systems and applications-1, 183-221, 2010 | 956 | 2010 |
| Model predictive control for optimal coordination of ramp metering and variable speed limits A Hegyi, B De Schutter, H Hellendoorn Transportation Research Part C: Emerging Technologies 13 (3), 185-209, 2005 | 778 | 2005 |
| Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms J Lago, F De Ridder, B De Schutter Applied Energy 221, 386-405, 2018 | 632 | 2018 |
| Optimal coordination of variable speed limits to suppress shock waves A Hegyi, B De Schutter, J Hellendoorn IEEE Transactions on intelligent transportation systems 6 (1), 102-112, 2005 | 504 | 2005 |
| Stability analysis and nonlinear observer design using Takagi-Sugeno fuzzy models Z Lendek, TM Guerra, R Babuska, B De Schutter Springer Berlin Heidelberg, 2011 | 447 | 2011 |
| Model predictive control for max-plus-linear discrete event systems B De Schutter, T Van Den Boom Automatica 37 (7), 1049-1056, 2001 | 420 | 2001 |
| DAISY: A database for identification of systems B De Moor, P De Gersem, B De Schutter, W Favoreel JOURNAL A 38 (4), 5, 1997 | 409 | 1997 |
| Development of advanced driver assistance systems with vehicle hardware-in-the-loop simulations O Gietelink, J Ploeg, B De Schutter, M Verhaegen Vehicle System Dynamics 44 (7), 569-590, 2006 | 396 | 2006 |
| Deep convolutional neural networks for detection of rail surface defects S Faghih-Roohi, S Hajizadeh, A Núñez, R Babuska, B De Schutter 2016 International joint conference on neural networks (IJCNN), 2584-2589, 2016 | 391 | 2016 |
| Residential demand response of thermostatically controlled loads using batch reinforcement learning F Ruelens, BJ Claessens, S Vandael, B De Schutter, R Babuška, ... IEEE Transactions on Smart Grid 8 (5), 2149-2159, 2016 | 345 | 2016 |
| Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark J Lago, G Marcjasz, B De Schutter, R Weron Applied Energy 293, 116983, 2021 | 324 | 2021 |
| Demand response with micro-CHP systems M Houwing, RR Negenborn, B De Schutter Proceedings of the IEEE 99 (1), 200-213, 2011 | 311 | 2011 |
| Optimal traffic light control for a single intersection B De Schutter, B De Moor European Journal of Control 4 (3), 260-276, 1998 | 297 | 1998 |
| Accelerated gradient methods and dual decomposition in distributed model predictive control P Giselsson, MD Doan, T Keviczky, B De Schutter, A Rantzer Automatica 49 (3), 829-833, 2013 | 292 | 2013 |
| Multi-agent model predictive control for transportation networks: Serial versus parallel schemes RR Negenborn, B De Schutter, J Hellendoorn Engineering Applications of Artificial Intelligence 21 (3), 353-366, 2008 | 290 | 2008 |
| Distributed model predictive control of irrigation canals RR Negenborn, PJ van Overloop, T Keviczky, B De Schutter Networks and heterogeneous media 4 (2), 359-380, 2009 | 281 | 2009 |
| A comparative analysis of distributed MPC techniques applied to the HD-MPC four-tank benchmark I Alvarado, D Limon, DM De La Peña, JM Maestre, MA Ridao, H Scheu, ... Journal of Process Control 21 (5), 800-815, 2011 | 258 | 2011 |
Hans HellendoornHoogleraar regeltechniek, TU Delft在 tudelft.nl 的电子邮件经过验证
