ID | 118137 |
Author |
Yoshida, Shogo
Tokushima University
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Content Type |
Journal Article
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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.
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Journal Title |
Journal of Signal Processing
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ISSN | 18801013
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Publisher | Research Institute of Signal Processing
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Volume | 21
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Issue | 4
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Start Page | 183
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End Page | 186
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Published Date | 2017-07-20
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Remark | 利用は著作権の範囲内に限られる。
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EDB ID | |
DOI (Published Version) | |
URL ( Publisher's Version ) | |
FullText File | |
language |
eng
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TextVersion |
Publisher
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departments |
Science and Technology
Technical Support Department
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