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- Gonzalo Joya Caparrós, Miguel A. Atencia Ruiz, Francisco Sandoval Hernández
- Neurocomputing
- 2002

This work summarizes a tutorial on the main aspects of the application of Hopfield networks to optimization. The main formulations of the dynamics are studied, and the particular problems that arise in their application to optimization are brought to light. As a particular engineering problem, systems identification is formulated as an optimization problem… (More)

- Miguel A. Atencia Ruiz, Gonzalo Joya Caparrós, Francisco Sandoval Hernández
- Neural Computation
- 2005

In this letter, the ability of higher-order Hopfield networks to solve combinatorial optimization problems is assessed by means of a rigorous analysis of their properties. The stability of the continuous network is almost completely clarified: (1) hyperbolic interior equilibria, which are unfeasible, are unstable; (2) the state cannot escape from the… (More)

- Miguel A. Atencia Ruiz, Gonzalo Joya Caparrós, Francisco Sandoval Hernández
- Neural Computing & Applications
- 2004

In this work, a novel method for on-line identification of non-linear systems is proposed based upon the optimisation methodology with Hopfield neural networks. The original Hopfield model is adapted so that the weights of the resulting network are time-varying. A rigorous analytical study proves that, under mild assumptions, the estimations provided by the… (More)

- Miguel A. Atencia Ruiz, Gonzalo Joya Caparrós, Francisco Sandoval Hernández
- Neural Processing Letters
- 2004

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 problems, but the weights and the biases of the resulting network are time-varying, since the target function… (More)

- Gonzalo Joya Caparrós, Miguel A. Atencia Ruiz, Francisco Sandoval Hernández
- Neurocomputing
- 1997

In this paper, we compare the performance of two different methods for the task of electrooculogram saccadic points classification in patients with Ataxia SCA2: Multilayer Perceptrons (MLP) and Random Forest. First we segment the recordings of 6 subjects into ranges of saccadic and non-saccadic points as the basis of supervised learning. Then, we randomly… (More)