| Artificial neural networks for control of a grid-connected rectifier/inverter under disturbance, dynamic and power converter switching conditions S Li, M Fairbank, C Johnson, DC Wunsch, E Alonso, JL Proao IEEE transactions on neural networks and learning systems 25 (4), 738-750, 2013 | 184 | 2013 |
| Training recurrent neural networks with the Levenberg–Marquardt algorithm for optimal control of a grid-connected converter X Fu, S Li, M Fairbank, DC Wunsch, E Alonso IEEE transactions on neural networks and learning systems 26 (9), 1900-1912, 2014 | 149 | 2014 |
| Control of a buck DC/DC converter using approximate dynamic programming and artificial neural networks W Dong, S Li, X Fu, Z Li, M Fairbank, Y Gao IEEE Transactions on Circuits and Systems I: Regular Papers 68 (4), 1760-1768, 2021 | 87 | 2021 |
| Neural-network vector controller for permanent-magnet synchronous motor drives: Simulated and hardware-validated results S Li, H Won, X Fu, M Fairbank, DC Wunsch, E Alonso IEEE transactions on cybernetics 50 (7), 3218-3230, 2019 | 83 | 2019 |
| An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances M Fairbank, S Li, X Fu, E Alonso, D Wunsch Neural Networks 49, 74-86, 2014 | 59 | 2014 |
| Value-gradient learning M Fairbank, E Alonso Neural Networks (IJCNN), The 2012 International Joint Conference on, 3062–3069., 2012 | 59 | 2012 |
| Simple and fast calculation of the second-order gradients for globalized dual heuristic dynamic programming in neural networks M Fairbank, E Alonso, D Prokhorov IEEE transactions on neural networks and learning systems 23 (10), 1671-1676, 2012 | 52 | 2012 |
| The divergence of reinforcement learning algorithms with value-iteration and function approximation M Fairbank, E Alonso The 2012 International Joint Conference on Neural Networks (IJCNN), 2012 | 49 | 2012 |
| Convolutional neural networks applied to high-frequency market microstructure forecasting J Doering, M Fairbank, S Markose 2017 9th computer science and electronic engineering (ceec), 31-36, 2017 | 45* | 2017 |
| An equivalence between adaptive dynamic programming with a critic and backpropagation through time M Fairbank, E Alonso, D Prokhorov IEEE transactions on neural networks and learning systems 24 (12), 2088-2100, 2013 | 44 | 2013 |
| A comparison of deep-learning methods for analysing and predicting business processes I Venugopal, J Töllich, M Fairbank, A Scherp 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | 39 | 2021 |
| Vector control of a grid-connected rectifier/inverter using an artificial neural network S Li, DC Wunsch, M Fairbank, E Alonso The 2012 International Joint Conference on Neural Networks (IJCNN), 1-7, 2012 | 35 | 2012 |
| An iterative optimization and learning-based IoT system for energy management of connected buildings Y Gao, S Li, Y Xiao, W Dong, M Fairbank, B Lu IEEE Internet of Things Journal 9 (21), 21246-21259, 2022 | 29 | 2022 |
| Reinforcement learning by value gradients M Fairbank arXiv preprint arXiv:0803.3539, 2008 | 28 | 2008 |
| Demand management in time-slotted last-mile delivery via dynamic routing with forecast orders M Abdollahi, X Yang, MI Nasri, M Fairbank European Journal of Operational Research 309 (2), 704-718, 2023 | 20 | 2023 |
| Value-Gradient Learning M Fairbank City University London, 2014 | 16 | 2014 |
| Efficient calculation of the Gauss-Newton approximation of the Hessian matrix in neural networks M Fairbank, E Alonso Neural Computation 24 (3), 607-610, 2012 | 16 | 2012 |
| The local optimality of reinforcement learning by value gradients, and its relationship to policy gradient learning M Fairbank, E Alonso arXiv preprint arXiv:1101.0428, 2011 | 14 | 2011 |
| Clipping in Neurocontrol by Adaptive Dynamic Programming M Fairbank, D Prokhorov, E Alonso IEEE Transactions on Neural Networks and Learning Systems 25 (10), 1909-1920, 2014 | 13 | 2014 |
| Approximating optimal control with value gradient learning M Fairbank, D Prokhorov, E Alonso Reinforcement learning and approximate dynamic programming for feedback …, 2012 | 12 | 2012 |
Eduardo AlonsoProfessor of Artificial Intelligence; City, University of LondonEmail yang diverifikasi di city.ac.uk