G. Joya

发表

Genetic algorithms for optimal placement of phasor measurement units in electrical networks

F. J. Marín, F. Sandoval, F. García-Lagos, 2003 .

A discrete gradient method to enhance the numerical behaviour of Hopfield networks

Gonzalo Joya, Francisco Sandoval, Miguel Atencia, 2015 .

Hopfield neural networks for optimization: study of the different dynamics

Gonzalo Joya, Francisco Sandoval, Miguel Atencia, 2002 .

Application of high-order hopfield neural networks to the solution of diophantine equations

Gonzalo Joya, Francisco Sandoval, Miguel Atencia, 1991 .

Identification of noisy dynamical systems with parameter estimation based on Hopfield neural networks

Gonzalo Joya, Francisco Sandoval, Miguel Atencia, 2013 .

The ratio of hidden HIV infection in Cuba.

G. Joya, M. Atencia, Esther García-Garaluz, 2013, Mathematical biosciences and engineering : MBE.

Hop " eld neural networks for optimization : study of the di ! erent dynamics

G. Joya, F. Sandoval, F. Sandoval, 2001 .

Uncertainty Quantiﬁcation through Dropout in Time Series Prediction by Echo State Networks

G. Joya, R. Stoean, M. Atencia, 2020 .

A Machine Learning hourly analysis on the relation the Ionosphere and Schumann Resonance Frequency

G. Joya, R. Stoean, Nuria Novas, 2023, Measurement.

Time Series Clustering with Deep Reservoir Computing

Claudio Gallicchio, Gonzalo Joya, Miguel A. Atencia Ruiz, 2020, ICANN.

Dynamic Clustering of Time Series with Echo State Networks

Ruxandra Stoean, Gonzalo Joya, Catalin Stoean, 2019, IWANN.

Modelling the HIV-AIDS Cuban Epidemics with Hopfield Neural Networks

Gonzalo Joya, Francisco Sandoval, Miguel Atencia, 2009 .

A hybrid unsupervised—Deep learning tandem for electrooculography time series analysis

Ruxandra Stoean, Francisco García-Lagos, Gonzalo Joya, 2020, PloS one.

Dynamical analysis of continuous higher-order hopfield networks for combinatorial optimization.

Gonzalo Joya, Francisco Sandoval, Miguel Atencia, 2005, Neural computation.

Ranking Information Extracted from Uncertainty Quantification of the Prediction of a Deep Learning Model on Medical Time Series Data

Gonzalo Joya, Ruxandra Stoean, Miguel Atencia, 2020 .

Search of the Shortest Path in a Communication Network with Fuzzy Cost Functions

Gonzalo Joya, Alicia Triviño-Cabrera, Lissette Valdés, 2021, Symmetry.

On the Prospective Use of Deep Learning Systems for Earthquake Forecasting over Schumann Resonances Signals

G. Joya, R. Stoean, Manuel Fernández-Ros, 2022, ITISE 2022.

Advances in computational intelligence

Ignacio Rojas, Gonzalo Joya, Andreu Catala, 2019, Neural Computing and Applications.

Spurious minima and basins of attraction in higher-order Hopfield networks

Gonzalo Joya, Francisco Sandoval, Miguel Atencia, 2003 .

Two or three things that we (intend to) know about Hopfield and Tank networks

F. Sandoval, G. Joya, M. Atencia, 2005 .

Differentiation of Saccadic Eye Movement Signals

Gonzalo Joya, Roberto Becerra-García, Rodolfo García-Bermúdez, 2021, Sensors.

Advances in computational intelligence

Ignacio Rojas, Joan Cabestany, Gonzalo Joya, 2013, Lecture Notes in Computer Science.

Convolutional Neural Network Learning Versus Traditional Segmentation for the Approximation of the Degree of Defective Surface in Titanium for Implantable Medical Devices

Ruxandra Stoean, Gonzalo Joya, Catalin Stoean, 2019, IWANN.

Study of Keysight 3458A Temperature Coefficient for Different Aperture Times in DCV Sampling Mode

F. Sandoval, F. García-Lagos, G. Joya, 2018, 2018 Conference on Precision Electromagnetic Measurements (CPEM 2018).

On Using Perceptual Loss within the U-Net Architecture for the Semantic Inpainting of Textile Artefacts with Traditional Motifs

Miguel A. Atencia Ruiz, G. Joya, C. Stoean, 2022, 2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC).

Deep Learning for the Detection of Frames of Interest in Fetal Heart Assessment from First Trimester Ultrasound

Ruxandra Stoean, Gonzalo Joya, Catalin Stoean, 2021, IWANN.

Uncertainty Quantification through Dropout in Time Series Prediction by Echo State Networks

Gonzalo Joya, Ruxandra Stoean, Miguel Atencia, 2020 .

Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals

Saeid Nahavandi, U. Rajendra Acharya, Abbas Khosravi, 2020, Sensors.

Unsupervised Learning as a Complement to Convolutional Neural Network Classification in the Analysis of Saccadic Eye Movement in Spino-Cerebellar Ataxia Type 2

Ruxandra Stoean, Gonzalo Joya, Catalin Stoean, 2019, IWANN.

Robustness of the hopfield estimator for identification of dynamical systems

Gonzalo Joya, Francisco Sandoval, Miguel Atencia, 2011, ICANN 2011.

A neural paradigm for controlling autonomous systems with reflex behaviour and learning capability

F. Sandoval, G. Joya, 1995 .