Nonlinear Parametric Model Identification using Genetic Algorithms

Authors:
L. M. Pedroso-Rodriguez;A. Marrero;H. Arazoza
Affiliations:
Instituto de Cibernetica, Matematica y Fisica, La Habana, Cuba;Departamento de Matematica Aplicada, Universidad de La Habana, Cuba;Departamento de Ecuaciones Diferenciales, Universidad de La Habana, Cuba
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

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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.