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
Parametric identification of robotic systems with stable time-varying Hopfield networks
Neural Computing and Applications
Modelling the HIV-AIDS Cuban Epidemics with Hopfield Neural Networks
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Estimation of the rate of detection of infected individuals in an epidemiological model
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
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The objective of the work described in this paper is twofold. On the one hand, the aim is to present and validate a model of Dengue fever for the Cuban case which is defined by a delay differential system. Such a model includes time-varying parameters, which are estimated by means of a method based upon Hopfield Neural Networks. This method has been successfully applied in both robotic and epidemiological models described by Ordinary Differential Equations. Therefore, on the other hand, an additional aim of this work is to assess the behaviour of this neural estimation technique with a delay differential system. Experimental results show the ability of the estimator to deal with systems with delays, as well as plausible parameter estimations, which lead to predictions that are coherent with actual data.