Application of Combined Prediction Model Based on Kalman Filter in Building Deformation Monitoring
According to the characteristics of building settlement monitoring data,combined with the advantages of Kalman filtering al-gorithm,BP neural network model and AR auto-regressive model in data noise reduction and data prediction,this paper proposes and constructs a new BP-AR settlement prediction model based on Kalman filtering.The main steps of building deformation prediction based on the combined prediction model are as follows:firstly,the original observation data are reduced by Kalman filtering algorithm to eliminate the influence of random noise error on the observation data;secondly,BP neural network model is used to model and pre-dict the noise reduction sequence;finally,AR model is used to model and predict the residual error.The combined prediction model proposed in this paper is verified by the actual building settlement monitoring data.The results show that compared with BP neural network model and BP-AR model,the combined prediction model proposed in this paper has higher prediction accuracy,effectively reduces the impact of noise,and has higher superiority.
buildingssettlement predictionKalman filteringBP neural network modelAR auto-regressive model
万方数据
维普
