Hopfield neural networks for optimization: study of the different dynamics G Joya, MA Atencia, F Sandoval Neurocomputing 43 (1-4), 219-237, 2002 | 313 | 2002 |

Hopfield neural networks for parametric identification of dynamical systems M Atencia, G Joya, F Sandoval Neural Processing Letters 21, 143-152, 2005 | 54 | 2005 |

Dynamical analysis of continuous higher-order Hopfield networks for combinatorial optimization M Atencia, G Joya, F Sandoval Neural Computation 17 (8), 1802-1819, 2005 | 49 | 2005 |

Advances in artificial neural networks and machine learning A Prieto, M Atencia, F Sandoval Neurocomputing 121, 1-4, 2013 | 38 | 2013 |

Parametric identification of robotic systems with stable time-varying Hopfield networks M Atencia, G Joya, F Sandoval Neural Computing & Applications 13, 270-280, 2004 | 38 | 2004 |

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 | 29 | 2020 |

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 | 28 | 2007 |

Gray box identification with Hopfield neural networks M Atencia, G Sandoval Revista Investigacion Operacional 25 (1), 54-60, 2004 | 22 | 2004 |

Identification of noisy dynamical systems with parameter estimation based on Hopfield neural networks M Atencia, G Joya, F Sandoval Neurocomputing 121, 14-24, 2013 | 19 | 2013 |

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 | 16 | 2015 |

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 | 15 | 2020 |

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 | 14 | 2011 |

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 | 14 | 2003 |

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 | 13 | 1997 |

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 | 13 | 1991 |

Hopfield networks: from optimization to adaptive control M Atencia, G Joya 2015 International Joint Conference on Neural Networks (IJCNN), 1-8, 2015 | 11 | 2015 |

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 | 10 | 2021 |

Uncertainty quantification through dropout in time series prediction by echo state networks M Atencia, R Stoean, G Joya Mathematics 8 (8), 1374, 2020 | 10 | 2020 |

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 | 9 | 2007 |

A formal model for definition and simulation of generic neural networks MA Atencia, G Joya, F Sandoval Neural Processing Letters 11, 87-105, 2000 | 9 | 2000 |