Artificial Road Load Generation Using Artificial Neural Networks University of Birmingham

Format:

Conference/Event

Author/Creator:

Ogunoiki, Ogunoiki, author.

Contributor:

Olatunbosun, Oluremi

Conference Name:

SAE 2015 World Congress & Exhibition (2015-04-21 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2015

Summary:

AbstractThis research proposes the use of Artificial Neural Networks (ANN) to predict the road input for road load data generation for variants of a vehicle as vehicle parameters are modified. This is important to the design engineers while the vehicle variant is still in the initial stages of development, hence no prototypes are available and accurate proving ground data acquisition is not possible. ANNs are, with adequate training, capable of representing the complex relationships between inputs and outputs. This research explores the implementation of the ANN to predict road input for vehicle variants using a quarter vehicle test rig. The training and testing data for this research are collected from a validated quarter vehicle model

Notes:

Vendor supplied data

Publisher Number:

2015-01-0639

Access Restriction:

Restricted for use by site license