Guarneri Paolo, Rocca Gianpiero, Gobbi Massimiliano
Department of Mechanical Engineering, Politecnicodi Milano, Technical University, 20156 Milano, Italy.
IEEE Trans Neural Netw. 2008 Sep;19(9):1549-63. doi: 10.1109/TNN.2008.2000806.
This paper deals with the simulation of the tire/suspension dynamics by using recurrent neural networks (RNNs). RNNs are derived from the multilayer feedforward neural networks, by adding feedback connections between output and input layers. The optimal network architecture derives from a parametric analysis based on the optimal tradeoff between network accuracy and size. The neural network can be trained with experimental data obtained in the laboratory from simulated road profiles (cleats). The results obtained from the neural network demonstrate good agreement with the experimental results over a wide range of operation conditions. The NN model can be effectively applied as a part of vehicle system model to accurately predict elastic bushings and tire dynamics behavior. Although the neural network model, as a black-box model, does not provide a good insight of the physical behavior of the tire/suspension system, it is a useful tool for assessing vehicle ride and noise, vibration, harshness (NVH) performance due to its good computational efficiency and accuracy.
本文探讨了利用递归神经网络（RNN）对轮胎/悬架动力学进行仿真。RNN由多层前馈神经网络衍生而来，通过在输出层和输入层之间添加反馈连接。最优网络架构源自基于网络精度和规模之间最优权衡的参数分析。神经网络可以使用在实验室从模拟道路轮廓（防滑钉）获得的实验数据进行训练。神经网络得到的结果在广泛的运行条件下与实验结果显示出良好的一致性。该神经网络模型可以有效地作为车辆系统模型的一部分，用于准确预测弹性衬套和轮胎的动力学行为。虽然神经网络模型作为一个黑箱模型，不能很好地洞察轮胎/悬架系统的物理行为，但由于其良好的计算效率和准确性，它是评估车辆行驶性能以及噪声、振动与声振粗糙度（NVH）性能的有用工具。
