采用遗传神经网络的轮轨力建模方法

Modelling of wheel-rail force based on genetic neural networks

摘要:

为解决轮轨力建模问题,提出了一种基于遗传算法和径向基函数神经网络的轮轨力建模方法,该方法基于轨道不平顺输入实现了轮轨力的预测．在径向基函数神经网络的中心、宽度和权值参数上,分别采用遗传算法、最大距离法和最小二乘法来确定,从而提高建模精度并减轻该算法的计算量,实现了快速准确的轮轨力神经网络建模．仿真试验结果表明:提出的轮轨力建模方法具有较高的预测性能．

Abstract:

To solve the modelling problem of wheel-rail force, a modelling approach based on the genetic algorithm and radial basis function neural network method is proposed, which can predict the output of wheel-rail force using the input of track irregularities. In order to improve the accuracy of the designed neural network and relieve the computational burden, the centers, widths and weights of the neural network are determined using the maximum distance measure, the least square method and genetic algorithm, respectively. The simulation results indicate that the proposed method can predict wheel-rail force with high precision.