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| ファイル | 記述 | サイズ | フォーマット | |
|---|---|---|---|---|
| cdc.2016.7799153.pdf | 324.95 kB | Adobe PDF | 見る/開く |
| タイトル: | Nonlinear model reduction by deep autoencoder of noise response data |
| 著者: | Kashima, Kenji    https://orcid.org/0000-0002-2963-2584 (unconfirmed) |
| 著者名の別形: | 加嶋, 健司 |
| 発行日: | Dec-2016 |
| 出版者: | IEEE |
| 誌名: | 2016 IEEE 55th Conference on Decision and Control (CDC) |
| 開始ページ: | 5750 |
| 終了ページ: | 5755 |
| 抄録: | In this paper a novel model order reduction method for nonlinear systems is proposed. Differently from existing ones, the proposed method provides a suitable non-linear projection, which we refer to as control-oriented deep autoencoder (CoDA), in an easily implementable manner. This is done by combining noise response data based model reduction, whose control theoretic optimality was recently proven by the author, with stacked autoencoder design via deep learning. |
| 著作権等: | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 |
| URI: | http://hdl.handle.net/2433/263912 |
| DOI(出版社版): | 10.1109/cdc.2016.7799153 |
| 出現コレクション: | 学術雑誌掲載論文等 |
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