Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/20.500.12104/62978
Título: A wind speed neural model with particle swarm optimization Kalman learning
Autor: Alanis, A.Y.
Simetti, C.
Ricalde, L.J.
Odone, F.
Fecha de publicación: 2012
Resumen: This paper deals with a novel training algorithm for a neural network architecture for wind speed time series prediction. The proposed training algorithm is based in an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters The EKF-PSO based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed time series. The proposed model is trained and tested using real wind speed data values. In order to show the applicability of the proposed scheme Simulation results are included. © 2012 TSI Press.
URI: http://hdl.handle.net/20.500.12104/62978
Aparece en las colecciones:Producción científica UdeG (prueba)

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