基于 GA-BP 网络的矿山路面不平度辨识

摘要

BP neural network optimized by GA was used to identify the mining road.A fourteen degree-of-freedom vehicle vibration model was set up.The vehicle seat acceleration obtained by simu-lation was regarded as an ideal input sample of neural network,and the fitting road roughness was re-garded as an ideal output sample of neural network based on inverse transformation principles,then the nonlinear mapping model between them was built by network training.Road roughness was iden-tified under the conditions of different grade roads through fitting,various pit roads and different loads of dump truck.Identification ability was verified for complex mining roads due to high correla-tion coefficient and small relative error in this method.The accuracy of the method was verified through vehicle road test.Compared with simulation results of ride comfort under common C-class roads,it is shown that identification road is more closer to actual one,so as to achieve the purpose of improving the simulation accuracy of the models.%提出利用经遗传算法优化的 BP 神经网络辨识矿山路面的方法。建立了14自由度自卸车仿真模型，将仿真得到的座椅加速度作为网络理想输入样本，基于逆变换原理拟合出的路面不平度作为网络理想输出样本，通过网络训练，建立了两者之间非线性映射模型。对拟合出的不同等级路面、各种凹坑路面及自卸车不同载重下路面不平度进行辨识，辨识路面与测试路面相关系数高、相对误差小，验证了该方法具有对复杂矿山路面的辨识能力。通过整车道路试验，证明了该方法的准确性。与自卸车常用 C 级路面下的平顺性仿真结果的对比显示，采用该方法得到辨识路面更加接近实际路面，达到了提高模型仿真精度的目的。