%0 Journal Article
%T 基于RBF神经网络的轮胎滚动阻力建模研究
%A 毛建清
%A 毛鑫昕
%A 王东哲
%J -
%D 2019
%R 10.12136/j.issn.1000-890X.2019.10.0739
%X 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。;A tire rolling resistance model based on radial basis function（RBF）neural network was established，which maked 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 had greater advantages in training and prediction results，and could effectively predict tire rolling resistance
%K [轮胎滚动阻力
%K 模型
%K 径向基函数神经网络
%K 反向传播算法神经网络
%K 轮胎滚动阻力
%K 模型
%K 径向基函数神经网络
%K 反向传播算法神经网络
%K tire rolling resistance
%K model
%K RBF neural network
%K BP neural network]
%U http://www.rubbertire.cn/gy/xjgy/ch/reader/view_abstract.aspx?file_no=XJ2019-0231&flag=1