Evaluation Method of Seat Comfort for High-Speed Trains Based on Seat Ergonomic Parameters
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摘要: 为了降低高速列车舒适度调查的成本，避免进行大量的问卷调查和统计分析，对高速列车座椅静态舒适度评价方法进行了研究. 首先，通过确定高速列车座椅舒适度评价指标和指标权重，获得座椅舒适度计算方法；其次，利用BP神经网络构建以高速列车座椅8项人机几何参数为输入、以座椅舒适度评价为输出的座椅静态舒适度评价模型；最后，进行实例研究，对构建的神经网络评价模型进行训练和验证，并对神经网络进行权值和阈值的提取，构建神经网络的数学表达公式. 研究结果表明：当神经网络为1个隐含层、13个节点时，训练达到误差均值2.13 × 10−3、均方误差6.091 × 10−6的理想效果，且不存在过拟合现象；利用CHR2的一、二等座椅人机几何参数测量数据及对应舒适度评价对该网络进行验证，验证显示一等座的神经网络预测值跟实际评价值误差为3.07%，二等座评价误差为1.42%，该网络模型预测精度较高，且优于多元回归模型预测的结果.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.
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Key words:
- high-speed train /
- seat comfort /
- neural network /
- evaluation model
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表 1 座椅舒适度评价方法及特点
Table 1. Methods and characteristics of seat comfort evaluation
名称 手段 文献 相关研究 特点 主观评价法 量表法 [4] 提出采用 11 种描述的
10 点量表评价座椅舒适度样本数量多；样本选择影响结果；量表及问卷设计影响结果 问卷法 [5] 建立汽车座椅不舒适性问卷，
归纳 20 个影响舒适性因素客观评价法 坐姿分析 [6] 表明受试者主观不适感的增加，其坐姿的
变换频率和运动频率也随之增大耗时少；被试样本需求少；
可靠性高体压分布 [7] 提出最舒适的姿势对应的座椅压力分布
是椎间盘的压力最低，且通过理想的
压力分布来确定座椅的形状肌电测试 [8] 研究体位和坐姿的肌电变化，提出
改善使用者的舒适性和安全性必须
减少姿势肌肉的生物力学负荷主客观相结合 多元回归 [9] 建立主观评价与体压分布之间的数学模型 具有预测功能；预测效果跟样本数量、样本质量相关 神经网络 [10] 构建神经网络预测模型，输入界面压力、
3 个人体尺寸、座椅美观评价等指标，
输出变量为综合舒适性指数[11] 构建以 8 个座椅压力分布参数和 2 个
人体参数为输入的模糊神经网络模型，
评价带有主观性的座椅舒适性[12] 应用人工神经网络技术，以座椅各部分
特征为输入变量，建立了汽车座椅
舒适度的人工智能评价系统向量回归 [13] 提出以 14 个压力分布指标和 3 个人体
变量为输入，舒适度为输出的支持向量
回归的预测座椅主观舒适度方法
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表 2 座椅人机几何参数舒适度权重
Table 2. Comfort weights of seat ergonomic parameters
座高 座深 座宽 靠背高 靠背宽 容膝距 靠背倾角 座间距 0.257 0.089 0.093 0.076 0.059 0.273 0.096 0.057
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表 3 不同隐含层下不同节点数网络训练误差均值、均方误差
Table 3. Mean error and mean square error of network training with different hidden layers and numbers of nodes
节点数 1个隐含层 2个隐含层 误差均值/ × 10−3 均方误差/ × 10−5 误差均值/ × 10−3 均方误差/ × 10−5 5 4.635 5.969 4.340 2.715 6 4.210 6.492 4.804 10.001 7 2.954 1.516 4.520 2.937 8 3.586 2.286 5.907 6.177 9 3.489 1.686 2.660 0.992 10 3.710 2.521 4.390 3.118 11 3.460 2.102 8.090 14.641 12 3.407 2.797 3.650 2.208 13 2.130 0.609 3.841 1.857 14 3.397 2.180 6.660 11.361 15 6.420 11.073 4.330 11.073
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表 4 BP神经网络验证结果
Table 4. Verification results of BP neural network
项目 一等座结果 二等座结果 实际评分 5.456 5.096 验证输出值 5.289 5.024 绝对误差 −0.167 −0.072 相对误差百分比/% 3.07 1.42 回归分析误差/% 9.11 19.96
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