Modelling the HIV-AIDS Cuban Epidemics with Hopfield Neural Networks

Authors:
M. Atencia;G. Joya;F. Sandoval
Affiliations:
Departamento de Matemática Aplicada, E.T.S.I.Informática, Germany;Departamento de Tecnología Electrónica, E.T.S.I.Telecomunicación Universidad de Málaga, Málaga, Spain 29071;Departamento de Tecnología Electrónica, E.T.S.I.Telecomunicación Universidad de Málaga, Málaga, Spain 29071
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

Citing 2
Cited 4

Some new trends in identification and modeling of nonlinear dynamical systems

Applied Mathematics and Computation - Special issue on dynamics and control
System identification (2nd ed.): theory for the user

System identification (2nd ed.): theory for the user

Hopfield Neural Networks for Parametric Identification of Dynamical Systems

Neural Processing Letters
System Identification of Dengue Fever Epidemics in Cuba

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Robustness of the "hopfield estimator" for identification of dynamical systems

IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Identification of noisy dynamical systems with parameter estimation based on Hopfield neural networks

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work, Hopfield neural networks are applied to estimation of parameters in a dynamical model of Cuban HIV-AIDS epidemics. The time-varying weights are derived, and its formulation is adapted to the discrete case. The method is tested on a data sequence obtained from numerical solution of the model. Simulation results show that the proposed technique quickly reduces the output prediction error, and it adapts well to parameter changes. Results concerning estimation error are poor, and some directions to deal with this issue are proposed.