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Summary & Details

Parameter Identification of a Driveline System by Deep Neural Networks

Delivery: Available on this site

Format: PDF

Price: Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880

Publication code: 20215334

Paper/Info type: Proceedings (Spring)

No.73-21

Pages: 1-7(Total 7 p)

Date of publication: May 2021

Publisher: JSAE

Language: English

Event: 2021 JSAE Annual Congress (Spring)[Online Meeting]

Detailed Information

Author(E)	1) Davide Gorgoretti, 2) Wouter Vandermeulen, 3) Toshio Fuwa
Affiliation(E)	1) Siemens Digital Industries Software, 2) Siemens Digital Industries Software, 3) Toyota Motor
Abstract(E)	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.

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