Abstract:According to the non-linearity, volatility characteristics and real-time dynamic data processing of deformation monitoring data, the auto-regressive and moving average model (ARMA) is used to construct the trend, based on the selection of variance compensation adaptive Kalman filter for stochastic disturbance rejection and model error weakening analysis. The error compensation and correction ARMA model is obtained by using particle swarm optimization (PSO) parameter optimization support vector machine (SVM). The method is used to predict the deformation monitoring engineering. The prediction results show that the method can describe the actual deformation of engineering under complex environmental factors and play a certain reference value in forecasting the project.
RONG Jing,LIU Lilong,KANG Haohua et al. The Deformation Prediction of ARMA and PSO-SVM Model Based on Variance Compensation Adaptive Kalman Filter[J]. jgg, 2018, 38(7): 689-694.