Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Gonzalo Joya"'
Autor:
Francisco García-Lagos, Ruxandra Stoean, Luis Velázquez-Pérez, Roberto Becerra-García, Gonzalo Joya, Miguel Atencia, Rodolfo García-Bermúdez, Catalin Stoean
Publikováno v:
PLoS ONE
PLoS ONE, Vol 15, Iss 7, p e0236401 (2020)
PLoS ONE, Vol 15, Iss 7, p e0236401 (2020)
Medical data are often tricky to get mined for patterns even by the generally demonstrated successful modern methodologies of deep learning. This paper puts forward such a medical classification task, where patient registers of two of the categories
Publikováno v:
Neurocomputing. 121:14-24
An algorithm for estimating time-varying parameters of dynamical systems is proposed, within the large family of prediction error methods. The algorithm is based on the ability of Hopfield neural networks to solve optimisation problems, since its for
Publikováno v:
Mathematical and Computer Modelling. 57:2971-2983
In this paper, a method of parameter estimation, based upon Hopfield neural networks, is applied to the identification of a model of the HIV/AIDS epidemic in Cuba. This estimation technique presents a number of features that make it especially suitab
Autor:
Gonzalo Joya, Javier Diaz de Aguilar, Francisco García-Lagos, Francisco Sandoval, Rado Lapuh, J. R. Salinas
Publikováno v:
2016 Conference on Precision Electromagnetic Measurements (CPEM 2016).
In this paper we present a new method based on ADALINE artificial neural networks (ANNs) for the spectra analysis and frequencies estimation of signals with harmonic and interharmonic content. First tests indicate that the method is able to obtain hi
Publikováno v:
Neurocomputing. 72:3640-3648
A new method designed to perform high-accuracy spectral analysis, based on ADALINE artificial neural networks (ANNs), is proposed. The proposed network is able to accurately calculate the fundamental frequency and the harmonic content of an input sig
Publikováno v:
Neural Computing and Applications. 19:139-150
In this paper, different neural network-based solutions to the contingency analysis problem are presented. Contingency analysis is examined from two perspectives: as a functional approximation problem obtaining a numerical evaluation and ranking cont
Publikováno v:
Neurocomputing. 70:2828-2835
The aim of this contribution is to implement a hardware module that performs parametric identification of dynamical systems. The design is based upon the methodology of optimization with Hopfield neural networks, leading to an adapted version of thes
Autor:
Gonzalo Joya, Miguel Atencia
Publikováno v:
IJCNN
This paper proposes an adaptive control algorithm, which is designed by adding a parametric identification method to a non-linear controller. The identification module is built upon the Hopfield neural network, resulting in an unconventional network
Publikováno v:
Neural Processing Letters. 21:143-152
In this work, a novel method, based upon Hopfield neural networks, is proposed for parameter estimation, in the context of system identification. The equation of the neural estimator stems from the applicability of Hopfield networks to optimization p
Publikováno v:
Neurocomputing. 43:219-237
In this paper the application of arbitrary order Hop"eld-like neural networks to optimization problems is studied. These networks are classi"ed in three categories according to their dynamics, expliciting the energy function for each category. The ma