Abstract：Least squares estimation and partial errors-in variables total least squares donot have the ability to resist gross errors. As gross error may also appear in the observed value and the coefficient matrix in differential equations, this paper puts forward a partial errors-invariables total least squares model based on IGGⅢ differential resistance. This paper also compares the robust least squares,partial errors-in variables total least squares with the new algorithm systematically,usingparameter estimation results, stability through simulation data, and high-speed railway observations data. The results show that the new algorithm's accuracy is high, which can be applied to the high-speed railway subsidence prediction.
CHEN Yang,WEN Hongyan,QIN Hui et al. Robust Total Least Squares Estimated in GM(1,1) for High-Speed Railway Foundation Deformation Prediction[J]. jgg, 2018, 38(2): 140-146.
