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Proceedings Paper

Evaluation of vehicle ride comfort based on neural network
Author(s): Yinhan Gao; Rongjiang Tang; Jie Liang
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Paper Abstract

The relationship between subjective ride comfort in a vehicle seat and human whole-body vibration can be modeled using frequency weightings and rms(root mean square) averaging as specified in ISO2631. However, recent studies indicate that, there are some flaws in the relationship between subjective response and objective vibration given by the ISO2631.This paper presents an alternative approach based on neural network model. Time-domain vibration acceleration signals are processed as neural network inputs, subjective evaluation results are quantified as outputs, and the weights of neural networks are used as frequency weighting coefficients to evaluate the vehicle ride comfort. The method has been used to evaluate the ride comfort on a number of conditions with good results achieved.

Paper Details

Date Published: 28 December 2010
PDF: 6 pages
Proc. SPIE 7544, Sixth International Symposium on Precision Engineering Measurements and Instrumentation, 754407 (28 December 2010); doi: 10.1117/12.885814
Show Author Affiliations
Yinhan Gao, Jilin Univ. (China)
Rongjiang Tang, Jilin Univ. (China)
Jie Liang, Jilin Univ. (China)


Published in SPIE Proceedings Vol. 7544:
Sixth International Symposium on Precision Engineering Measurements and Instrumentation
Jiubin Tan; Xianfang Wen, Editor(s)

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