Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12104/39165
Title: A wind speed neural model with particle swarm optimization Kalman learning
Author: Alanis, A.Y.
Simetti, C.
Ricalde, L.J.
Odone, F.
Issue Date: 2012
Abstract: 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://www.scopus.com/inward/record.url?eid=2-s2.0-84870813365&partnerID=40&md5=d3862171a6ab8feb83bb0b49cfb27c3a
http://hdl.handle.net/20.500.12104/39165
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.