基于改进GM(1,1)与WOA-LSSVM组合预测模型的轨道不平顺预测

在保障列车行车安全的前提下对轨道不平顺的发展趋势进行预测,可以提高线路维护效率。根据轨检车的历史轨检TQI数值进行分析,提出一种基于非等时距近似非齐次的GM(1,1)模型与鲸鱼算法优化的最小二乘支持向量机的组合预测模型。对非等时距GM(1,1)模型的灰作用量进行优化,并设置加权矩阵,对不同检测时间的数据赋予不同权值,建立非等时距近似非齐次的GM(1,1)模型,得到初步预测值。在此基础上,利用鲸鱼算法优化的最小二乘支持向量机(WOA-LSSVM)对残差进行修正,得到最终预测值。分别对某线上行两段线路的轨道不平顺TQI值进行预测,结果表明:该预测方法相对误差平均值分别为2. 316%和1. 67%,后验差分别为0. 093和0. 068,精度等级达到1级,实现了轨道不平顺较高精度的预测。

Prediction of Track Irregularity Based on Improved GM(1,1)and WOA-LSSVM Combination Model

Under the premise of protecting the safety of train traffic,effective prediction of irregularity development trend can improve the efficiency of line maintenance. According to the analysis of historical track quality data from track inspection car,this paper proposes a combined forecasting model based on the non-equal interval and non-homogeneous GM( 1,1) model of the least squares support vector machine optimized by whale optimization algorithm. The grey effect amount of Non-equal interval GM( 1,1) model is optimized and a weighting matrix is set up to assign different weights to data obtained at different detection times. Non-equal interval and non-homogeneous GM( 1,1) model is established to get the preliminary prediction value. Based on this,the residual error is corrected by using the least squares support vector machine( WOA-LSSVM) optimized by whale optimization algorithm, and the final prediction value is obtained. The track irregularity TQI values of two up-direction sections on a certain line are predicted,and the results show that the averages of the relative errors of this prediction method are 2. 316% and 1. 67% respectively,the posterior error ratios are 0. 093 3 and 0. 068 1 respectively,and the accuracy grade reaches level 1. Thus,accurate prediction of track irregularity is fulfilled