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.
This work has been partially supported by the Spanish Ministerio de Ciencia y Tecnología (MCYT), Project No. TIC2001-1758. Thanks are due to Hector de Arazoza for providing the model and Liuva Pedroso for generating test data.
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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. https://doi.org/10.1007/3-540-44869-1_57
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DOI: https://doi.org/10.1007/3-540-44869-1_57
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