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Nonlinear Parametric Model Identification using Genetic Algorithms

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Artificial Neural Nets Problem Solving Methods (IWANN 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2687))

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Abstract

This paper proposes an heuristic approach based on genetic algorithms to obtain numerical solutions for the identification problem in deterministic dynamical systems given a set of discrete observations of the model. The ordinary differential equations system is solved using an appropriate numerical integrator and an error function is minimized using a genetic algorithm. Experiments were designed for a model of HIV-AIDS epidemic evolution in Cuba.

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References

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Author information

Authors and Affiliations

  1. Instituto de Cibernetica, Matematica y Fisica, La Habana, Cuba

    L. M. Pedroso-Rodriguez

  2. Departamento de Matematica Aplicada, Universidad de La Habana, Cuba

    A. Marrero

  3. Departamento de Ecuaciones Diferenciales, Universidad de La Habana, Cuba

    H. de Arazoza

Authors
  1. L. M. Pedroso-Rodriguez
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  2. A. Marrero
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  3. H. de Arazoza
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Editor information

Editors and Affiliations

  1. E.T.S. de Ingeniería Informática Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia, Juan del Rosal, 16, 28040, Madrid, Spain

    José Mira  & José R. Álvarez  & 

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© 2003 Springer-Verlag Berlin Heidelberg

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Pedroso-Rodriguez, L.M., Marrero, A., de Arazoza, H. (2003). Nonlinear Parametric Model Identification using Genetic Algorithms. 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_60

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  • DOI: https://doi.org/10.1007/3-540-44869-1_60

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40211-4

  • Online ISBN: 978-3-540-44869-3

  • eBook Packages: Springer Book Archive

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