To fully consider the influences of the exciting frequency (0–20 Hz) and the environmental temperature (−50–20°C) on the dynamic characteristics of the rubber bushing in the railway vehicle, a hybrid neural network model is applied to develop the detailed model of the rubber bushing. The nonlinearities of the rubber bushing existing in the elastic element, the friction element and the viscous element are involved in the model to reflect the frequency- and amplitude-dependent characteristics of the rubber bushing. The experimental data are collected and exploited to find out the relationship between the input and output with the help of the neural network model. Furthermore, the influences of the environmental temperature on the dynamic parameters of the rubber bushing are considered. By testing the dynamic performance of the rubber bushing under the excitation with different frequencies, amplitudes and temperatures, the hybrid neural network model is trained and its critical parameters are identified. In this way, the trained hybrid neural network model for the rubber bushing can accurately reflect the dynamic performance in the presence of various excitation and environment temperatures.

为充分考虑励磁频率（0~20 Hz）和环境温度（-50~20°C）对轨道车辆橡胶衬套动态特性的影响，采用混合神经网络模型开发橡胶衬套的详细模型。模型中涉及了橡胶衬套在弹性元件、摩擦元件和粘性元件中存在的非线性，以反映橡胶衬套的频率和振幅相关特性。收集和利用实验数据，在神经网络模型的帮助下找出输入和输出之间的关系。此外，还考虑了环境温度对橡胶衬套动态参数的影响。通过测试橡胶衬套在不同频率、振幅和温度激励下的动态性能，训练混合神经网络模型并识别其关键参数。这样，经过训练的橡胶衬套混合神经网络模型可以准确反映在各种激励和环境温度下的动态性能。