NDLI: Higher Order Wavelet Neural Networks with Kalman learning for wind speed forecasting

Content Provider	IEEE Xplore Digital Library
Author	Ricalde, L.J. Catzin, G.A. Alanis, A.Y. Sanchez, E.N.
Copyright Year	2011
Description	Author affiliation: CINVESTAV, Unidad Guadalajara, Apdo. Postal 31-438, Plaza La Luna, Guadalajara, Jalisco, C.P. 45091, Mexico (Sanchez, E.N.) \|\| UADY, Faculty of Engineering, Apdo. Postal 115, Cordemex, Merida, Yucatan, Mexico (Ricalde, L.J.; Catzin, G.A.) \|\| CUCEI, Universidad de Guadalajara, Apdo. Postal 51-71, 45080, Zapopan, Jalisco, Mexico (Alanis, A.Y.)
Abstract	In this paper, a Higher Order Wavelet Neural Network (HOWNN) trained with an Extended Kalman Filter (EKF) is implemented to solve the wind forecasting problem. The Neural Network based scheme is composed of high order terms in the input layer, two hidden layers, one incorporating radial wavelets as activation functions and the other using classical logistic sigmoid, and an output layer with a linear activation function. A Kalman filter based algorithm is employed to update the synaptic weights of the wavelet network. The size of the regression vector is determined by means of the Lipschitz quotients method. 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.
Starting Page	1
Ending Page	6
File Size	288622
Page Count	6
File Format	PDF
ISBN	9781424498932
e-ISBN	9781424498949
DOI	10.1109/CIASG.2011.5953332
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-11
Publisher Place	French Guiana
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Wind speed Wavelet network Time series analysis Neural networks Wavelets functions Artificial neural networks Kalman filtering Covariance matrix Wind forecast Kalman filters Equations
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A wind speed neural model with particle swarm optimization Kalman learning

Forecasting for smart grid applications with Higher Order Neural Networks

An artificial neural network hybrid with wavelet transform for short-term wind speed forecasting: A preliminary case study

On the Kalman filtering method in neural network training and pruning

Wavelet recurrent neural network with semi-parametric input data preprocessing for micro-wind power forecasting in integrated generation Systems

Wind speed forecasting based on Time series - Adaptive Kalman filtering algorithm

The deformation time series prediction based on wavelet and neural network

Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks

Roles of learning rates, artificial process noise and square root filtering for extended Kalman filter training

Higher Order Wavelet Neural Networks with Kalman learning for wind speed forecasting

Similar Documents

A wind speed neural model with particle swarm optimization Kalman learning

Forecasting for smart grid applications with Higher Order Neural Networks

An artificial neural network hybrid with wavelet transform for short-term wind speed forecasting: A preliminary case study

On the Kalman filtering method in neural network training and pruning

Wavelet recurrent neural network with semi-parametric input data preprocessing for micro-wind power forecasting in integrated generation Systems

Wind speed forecasting based on Time series - Adaptive Kalman filtering algorithm

The deformation time series prediction based on wavelet and neural network

Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks

Roles of learning rates, artificial process noise and square root filtering for extended Kalman filter training

Higher Order Wavelet Neural Networks with Kalman learning for wind speed forecasting