Evaluation Method of Seat Comfort for High-Speed Trains Based on Seat Ergonomic Parameters

WANG Jin; ZHI Jinyi; XIANG Zerui; LI Ran; XU Xiaofei; YAN Lei; XU Gang

doi:10.3969/j.issn.0258-2724.20180499

Volume 54 Issue 6

Nov. 2019

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Journal of Southwest Jiaotong University > 2019 > 54(6): 1342-1348.

WANG Jin, ZHI Jinyi, XIANG Zerui, LI Ran, XU Xiaofei, YAN Lei, XU Gang. Evaluation Method of Seat Comfort for High-Speed Trains Based on Seat Ergonomic Parameters[J]. Journal of Southwest Jiaotong University, 2019, 54(6): 1342-1348. doi: 10.3969/j.issn.0258-2724.20180499

Citation:

WANG Jin, ZHI Jinyi, XIANG Zerui, LI Ran, XU Xiaofei, YAN Lei, XU Gang. Evaluation Method of Seat Comfort for High-Speed Trains Based on Seat Ergonomic Parameters[J]. Journal of Southwest Jiaotong University, 2019, 54(6): 1342-1348. doi: 10.3969/j.issn.0258-2724.20180499

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Evaluation Method of Seat Comfort for High-Speed Trains Based on Seat Ergonomic Parameters

doi: 10.3969/j.issn.0258-2724.20180499

WANG Jin^1
,,
ZHI Jinyi^{1,2
,
,},
XIANG Zerui^1,2,
LI Ran^1,2,
XU Xiaofei^1,2,
YAN Lei³,
XU Gang³

Received Date: 13 Jun 2018
Rev Recd Date: 13 Sep 2018

Available Online: 20 Sep 2018

Publish Date: 01 Dec 2019

Abstract

Abstract

To reduce the cost in the comfort evaluation of high-speed trains and avoid strenuous questionnaire investigation and statistical analysis, the static comfort evaluation method of high speed train seats is studied. Firstly, the seat comfort calculation method is derived by determining the comfort evaluation index and index weight for high-speed train seats. Secondly, the BP neural network is used to construct a static seat comfort evaluation model, which takes the 8 ergonomic parameters of the high-speed train seat as the input and the seat comfort evaluation as the output. Finally, a case study is carried out to train and verify the constructed neural network evaluation model, and the weights and thresholds of the neural network are extracted to construct a mathematical expression of the neural network. The results show that when the neural network has one hidden layer and 13 nodes, the training achieves the desirable results with the mean error of 2.13×10⁻³ and mean square error of 6.091×10⁻⁶, and there is no over fitting. The network is verified by the real ergonomic data of the first-class and second-class seats in CHR2 and the corresponding comfort evaluations. The error of first-class seats between the predicted value of neural network and the actual one is 3.07%, and the error of second-class seats is 1.42%, demonstrating that the network model has high prediction accuracy and is superior to the multiple regression model.
- high-speed train,
- seat comfort,
- neural network,
- evaluation model

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References(21)

References

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