System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Hopfield Neural Networks for Parametric Identification of Dynamical Systems
Neural Processing Letters
Hopfield neural networks for on-line parameter estimation
Neural Networks
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
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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In previous work, a method for estimating the parameters of dynamical systems was proposed, based upon the stability properties of Hopfield networks. The resulting estimation is a dynamical system itself, and the analysis of its properties showed, under mild conditions, the convergence of the estimates towards the actual values of parameters. Also, it was proved that in the presence of noise in the measured signals, the estimation error remains asymptotically bounded. In this work, we aim at advancing in this robustness analysis, by considering deterministic disturbances, which do not fulfill the usual statistical hypothesis such as normality and uncorrelatedness. Simulations show that the estimation error asymptotically vanishes when the disturbances are additive. Thus the form of the perturbation affects critically the dynamical behaviour and magnitude of the estimation, which is a significant finding. The results suggest a promising robustness of the proposed method, in comparison to conventional techniques.