Abstract:According to the nonlinear characteristics of the deformation monitoring data of high speed railways, a combined forecasting model based on wavelet and gray support vector machine is proposed.Using wavelet analysis to obtain the random sequence and approximate sequence of different time-frequency scales,through the determination of embedding dimension and the correlation analysis of high and low frequency data,the reconstructed random sequence is used as the genetic algorithm to optimize the input of the SVR model.The grey support vector machine is then used to describe the evolution of the approximate sequence. Finally, the two prediction results are coupled and superimposed,and the result of the combined model of wavelet-gray-support vector machine (SVM) is obtained.Taking the measured data of Guizhou-Guangzhou high-speed railway as an example, the mean square error, the mean absolute error and the mean absolute relative error are used to evaluate the prediction results.The example shows that the model fits the approximate component well, and avoids the over fitting of the detail components.
RONG Jing,LIU Lilong,GAN Xiangqian et al. High Speed Rail Deformation Prediction Based on Wavelet Analysis and Grey-Support Vector Machine[J]. jgg, 2018, 38(5): 473-476.