NDLI: A wind speed neural model with particle swarm optimization Kalman learning

Content Provider	IEEE Xplore Digital Library
Author	Alanis, Alma Y. Simetti, Chiara Ricalde, Luis J. Odone, Francesca
Copyright Year	2012
Description	Author affiliation: UADY, Faculty of Engineering, Av. Industrias no Contaminantes por Periferico Norte, Apdo. Postal 115 Cordemex, Merida, Yucatan, Mexico (Ricalde, Luis J.) \|\| DISI Università degli Studi di Genova, Via Dodedaneso 35 - 16146 Italy (Simetti, Chiara; Odone, Francesca) \|\| CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Col. las aguilas, C.P. 45080, Zapopan, Jalisco, Mexico (Alanis, Alma Y.)
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.
Starting Page	1
Ending Page	5
File Size	459330
Page Count	5
File Format	PDF
ISBN	9781467344975
ISSN	21544824
e-ISBN	9781889334479
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-24
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	TSI Press
Subject Keyword	Neural networks Wind speed Training Particle swarm optimization Vectors Time series analysis Wind forecasting neural identifier Wind forecast particle swarm optimization neural networks Kalman filtering learning
Content Type	Text
Resource Type	Article

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