Modelling the HIV-AIDS Cuban Epidemics with Hopfield Neural Networks

  • M. Atencia
  • G. Joya
  • F. Sandoval
Conference paper

DOI: 10.1007/3-540-44869-1_57

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2687)
Cite this paper as:
Atencia M., Joya G., Sandoval F. (2003) Modelling the HIV-AIDS Cuban Epidemics with Hopfield Neural Networks. In: Mira J., Álvarez J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • M. Atencia
    • 1
  • G. Joya
    • 2
  • F. Sandoval
    • 2
  1. 1.Departamento de Matemática AplicadaE.T.S.I.InformáticaGermany
  2. 2.Departamento de Tecnología ElectrónicaE.T.S.I.Telecomunicación Universidad de MálagaMálagaSpain

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