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ID 118137
Author
Yoshida, Shogo Tokushima University
Content Type
Journal Article
Description
This paper describes a prediction method for wind speed fluctuation using a deep belief network (DBN) trained with ensemble learning. In particular, we investigate the usefulness of the ensemble learning for an prediction accuracy improvement of wind speed fluctuation. Bootstrap aggregating (the bagging method), which is a typical algorithm of ensemble learning, has been applied to train the DBN. The prediction result is decided by a majority vote of each DBN output. In addition, two bagging methods with different selection methods of training data have been proposed. These proposed methods have been evaluated from several prediction results by comparison with a conventional method.
Journal Title
Journal of Signal Processing
ISSN
18801013
Publisher
Research Institute of Signal Processing
Volume
21
Issue
4
Start Page
183
End Page
186
Published Date
2017-07-20
Remark
利用は著作権の範囲内に限られる。
EDB ID
DOI (Published Version)
URL ( Publisher's Version )
FullText File
language
eng
TextVersion
Publisher
departments
Science and Technology
Technical Support Department