基于GA-BP网络的矿山路面不平度辨识

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。

Identification of Mining Road Roughness Based on GA-BP Neural Network

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 simulation was regarded as an ideal input sample of neural network, and the fitting road roughness was regarded 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 identified 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 correlation 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.