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Miguel Atencia
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Cited by
Year
Hopfield neural networks for optimization: study of the different dynamics
G Joya, MA Atencia, F Sandoval
Neurocomputing 43 (1-4), 219-237, 2002
3132002
Hopfield neural networks for parametric identification of dynamical systems
M Atencia, G Joya, F Sandoval
Neural Processing Letters 21, 143-152, 2005
542005
Dynamical analysis of continuous higher-order Hopfield networks for combinatorial optimization
M Atencia, G Joya, F Sandoval
Neural Computation 17 (8), 1802-1819, 2005
492005
Advances in artificial neural networks and machine learning
A Prieto, M Atencia, F Sandoval
Neurocomputing 121, 1-4, 2013
382013
Parametric identification of robotic systems with stable time-varying Hopfield networks
M Atencia, G Joya, F Sandoval
Neural Computing & Applications 13, 270-280, 2004
382004
Automated detection of presymptomatic conditions in Spinocerebellar Ataxia type 2 using Monte Carlo dropout and deep neural network techniques with electrooculogram signals
C Stoean, R Stoean, M Atencia, M Abdar, L Velázquez-Pérez, A Khosravi, ...
Sensors 20 (11), 3032, 2020
292020
FPGA implementation of a systems identification module based upon Hopfield networks
M Atencia, H Boumeridja, G Joya, F García-Lagos, F Sandoval
Neurocomputing 70 (16-18), 2828-2835, 2007
282007
Gray box identification with Hopfield neural networks
M Atencia, G Sandoval
Revista Investigacion Operacional 25 (1), 54-60, 2004
222004
Identification of noisy dynamical systems with parameter estimation based on Hopfield neural networks
M Atencia, G Joya, F Sandoval
Neurocomputing 121, 14-24, 2013
192013
A discrete gradient method to enhance the numerical behaviour of Hopfield networks
Y Hernández-Solano, M Atencia, G Joya, F Sandoval
Neurocomputing 164, 45-55, 2015
162015
Ranking information extracted from uncertainty quantification of the prediction of a deep learning model on medical time series data
R Stoean, C Stoean, M Atencia, R Rodríguez-Labrada, G Joya
Mathematics 8 (7), 1078, 2020
152020
Hopfield networks for identification of delay differential equations with an application to dengue fever epidemics in Cuba
E García-Garaluz, M Atencia, G Joya, F García-Lagos, F Sandoval
Neurocomputing 74 (16), 2691-2697, 2011
142011
Modelling the HIV-AIDS Cuban epidemics with Hopfield neural networks
M Atencia, G Joya, F Sandoval
International Work-Conference on Artificial Neural Networks, 449-456, 2003
142003
Associating arbitrary-order energy functions to an artificial neural network: Implications concerning the resolution of optimization problems
G Joya, MA Atencia, F Sandoval
Neurocomputing 14 (2), 139-156, 1997
131997
Application of high-order Hopfield neural networks to the solution of diophantine equations
G Joya, MA Atencia, F Sandoval
Artificial Neural Networks: International Workshop IWANN'91 Granada, Spain …, 1991
131991
Hopfield networks: from optimization to adaptive control
M Atencia, G Joya
2015 International Joint Conference on Neural Networks (IJCNN), 1-8, 2015
112015
Deep learning for the detection of frames of interest in fetal heart assessment from first trimester ultrasound
R Stoean, D Iliescu, C Stoean, V Ilie, C Patru, M Hotoleanu, R Nagy, ...
Advances in Computational Intelligence: 16th International Work-Conference …, 2021
102021
Uncertainty quantification through dropout in time series prediction by echo state networks
M Atencia, R Stoean, G Joya
Mathematics 8 (8), 1374, 2020
102020
Estimation of the rate of detection of infected individuals in an epidemiological model
M Atencia, G Joya, E García-Garaluz, H De Arazoza, F Sandoval
International Work-Conference on Artificial Neural Networks, 948-955, 2007
92007
A formal model for definition and simulation of generic neural networks
MA Atencia, G Joya, F Sandoval
Neural Processing Letters 11, 87-105, 2000
92000
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