| 基于RBF神经网络的轮胎滚动阻力建模研究 |
| Research on Tire Rolling Resistance Modeling Based on RBF Neural Network |
| 投稿时间：2019-07-20 修订日期：2019-07-20 |
| DOI：10.12136/j.issn.1000-890X.2019.10.0739 |
| 中文关键词: 轮胎 滚动阻力 模型 径向基函数 反向传播算法 神经网络 |
| 英文关键词: tire rolling resistance model RBF BP neural network |
| 基金项目:国家自然科学基金资助项目（61671099） |
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| 中文摘要: |
| 建立径向基函数（RBF）神经网络轮胎滚动阻力模型，充分利用RBF神经网络模型逼近精度高、训练速度快、无
局部极小等优点，对轮胎滚动阻力进行全面、准确的预测。结果表明，轮胎滚动阻力RBF与反向传播算法（BP）神经网络
模型预测值的平均相对误差分别为2%和6%左右，RBF神经网络模型在训练和预测结果上均有更大优势，能够有效预测
轮胎滚动阻力 |
| 英文摘要: |
| A tire rolling resistance model based on radial basis function （RBF）neural network was established，taking full use of the advantages of RBF network model，such as high approximation accuracy，
fast training speed and no local minimum，to predict tire rolling resistance comprehensively and accurately.
The results showed that the average relative errors of the prediction values of tire rolling resistance RBF
neural network model and back propagation（BP）neural network model were about 2% and 6%，respectively.
RBF neural network model showed advantages in training and prediction results，and could effectively predict
tire rolling resistance. |
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