Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12104/39792
Title: Bio-inspired aging model particle swarm optimization neural network training for solar radiation forecasting
Author: Rangel, E.
Alanis, A.Y.
Ricalde, L.J.
Arana-Daniel, N.
Lopez-Franco, C.
Issue Date: 2014
Abstract: 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. Zapotitlán Springer International Publishing Switzerland 2014.
URI: http://hdl.handle.net/20.500.12104/39792
http://www.scopus.com/inward/record.url?eid=2-s2.0-84921343423&partnerID=40&md5=7e6949f4d6dfc1ece2554074a1785583
Appears in Collections:Producción científica UdeG

Files in This Item:
There are no files associated with this item.


Items in RIUdeG are protected by copyright, with all rights reserved, unless otherwise indicated.