Parameter Identification of a Driveline System by Deep Neural Networks
- 提供方法
- 本サイト上にてダウンロード・閲覧可
- 形態
- 価格
- 一般価格(税込):¥1,100 会員価格(税込):¥880
- 文献番号
- 20215334
- 文献・情報種別
- 学術講演会予稿集(春)
No.73-21
- 掲載ページ
- 1-7(Total 7 p)
- 発行年月
- 2021年 5月
- 出版社
- (公社)自動車技術会
- 言語
- 英語
- イベント
- 2021年春季大会【オンライン開催】
書誌事項
著者(英) | 1) Davide Gorgoretti, 2) Wouter Vandermeulen, 3) Toshio Fuwa |
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勤務先(英) | 1) Siemens Digital Industries Software, 2) Siemens Digital Industries Software, 3) Toyota Motor |
抄録(英) | Identifying the physical parameters of a mechanical system is an essential process to correctly model its dynamics. Such identification usually relies on costly and time-consuming procedures, which may require both disassembly of system components and several physical measurements. This paper proposes a new methodology to carry out parameter identification in a fast and cost-effective way through Deep Neural Networks, which are trained to predict parameter values using data from the simulation model of the system under investigation. To highlight the viability of the proposed workflow, results are shown for a driveline system. 翻訳 |