Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12104/62978
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dc.contributor.authorAlanis, A.Y.
dc.contributor.authorSimetti, C.
dc.contributor.authorRicalde, L.J.
dc.contributor.authorOdone, F.
dc.date.accessioned2015-11-18T23:43:29Z-
dc.date.available2015-11-18T23:43:29Z-
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/20.500.12104/62978-
dc.description.abstractThis 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.
dc.titleA wind speed neural model with particle swarm optimization Kalman learning
dc.typeConference Paper
dc.relation.ispartofjournalWorld Automation Congress Proceedings
dc.subject.keywordKalman filtering learning; neural identifier; neural networks; particle swarm optimization; Wind forecast
dc.contributor.affiliationAlanis, A.Y., CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Col. las aguilas, C.P. 45080, Zapopan, Jalisco, Mexico; Simetti, C., DISI Università Degli Studi di Genova, Via Dodedaneso 35, 16146 Genova, Italy; Ricalde, L.J., UADY, Faculty of Engineering, Av. Industrias no Contaminantes por Periferico Norte, Cordemex, Merida, Yucatan, Mexico; Odone, F., DISI Università Degli Studi di Genova, Via Dodedaneso 35, 16146 Genova, Italy
dc.relation.isReferencedByScopus
dc.identifier.urlhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84870813365&partnerID=40&md5=d3862171a6ab8feb83bb0b49cfb27c3a
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