基于RBF网络的摆式客车横向未平衡离心加速度预测研究  

Prediction Study of Lateral Unbalance Centrifugal Acceleration of Tilting Coach Based on RBF Neural Networks

作　　者：张济民[1] 张继彤[1] 王开文[2] 

机构地区：[1]同济大学铁道与城市轨道交通研究院,上海200331 [2]西南交通大学牵引动力研究中心,成都610031

出　　处：《机床与液压》2005年第5期86-88,135,共4页Machine Tool & Hydraulics

基　　金：铁道部科技发展计划项目(99J45-E)

摘　　要：运用k-均值聚类法计算RBF神经网络隐含层神经元激活函数的中心及归一化参数,提出并运用了在线自适应RBF权矢量修正算法进行摆式客车通过曲线时离心加速度的多步预测。经过对实测信号的仿真分析证明:RBF神经网络可以以满意的精度对摆式客车离心加速度进行多步预测,预测的时间间隔大,有效解决了由于各种因素造成的滞后补偿问题,并且可以完全替代用于触发倾摆和判定曲线方向的陀螺仪的作用。k-means clustering algorithm was used to compute the center and normalizing parameters of the active function of the hidden unit of the RBF neural networks’ hidden layer,and the online self-adaptive algorithm with adjusting the RBF neural weight vector was put forward to predict the acceleration in multi-step.The data detected was simulated and the conclusion was that the RBF neural network can predict the centrifugal acceleration of the tilting train in the multi-step and that the lag result from all kinds reason may be compensated,and it can replace the gyroscope and its tiger function of the tilting and justify the curve direction completely.

关 键 词：摆式客车 离心加速度 RBF神经网络 k-均值聚类法 多步预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] U271[自动化与计算机技术—控制科学与工程]