By Topic

Locally recurrent neural networks optimal filtering algorithms: application to wind speed prediction using spatial correlation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Barbounis, T.G. ; Dept. of Electr. & Comput. Eng., Aristotle Univ. of Thessaloniki, Greece ; Theocharis, J.B.

This paper focuses on a locally recurrent multilayer network with internal feedback paths, the IIR-MLP. The computation of the partial derivatives of the network's output with respect to its trainable weights is achieved using backpropagation through adjoints and a second order global recursive prediction error (GRPE) training algorithm is developed. Also, a local version of the GRPE is presented in order to cope with the increased computational burden of the global version. The efficiency of the proposed learning schemes, as compared to conventional gradient based methods, is tested on the wind prediction problem from 15 min to 3 h ahead on a site, using spatial correlation and facilitating measurements from nearby sites up to 40 km away.

Published in:

Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on  (Volume:5 )

Date of Conference:

31 July-4 Aug. 2005