Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/20.500.12104/64561
Título: Bio-inspired aging model particle swarm optimization neural network training for solar radiation forecasting
Autor: Rangel, E.
Alanis, A.Y.
Ricalde, L.J.
Arana-Daniel, N.
Lopez-Franco, C.
Fecha de publicación: 2014
Resumen: This paper deals with a novel training algorithm for a neural network architecture applied to solar radiation time series prediction. The proposed training algorithm is based in a novel bio-inspired aging model-particle swarm optimization (BAM-PSO). The BAM-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 efficiently the complex nature of the solar radiation time series. The proposed model is trained and tested using real data values for solar radiation. © Springer International Publishing Switzerland 2014.
URI: http://hdl.handle.net/20.500.12104/64561
Aparece en las colecciones:Producción científica UdeG (prueba)

Ficheros en este ítem:
No hay ficheros asociados a este ítem.


Los ítems de RIUdeG están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.