Application of SSA-Elman Neural Network Model in Building Settlement Prediction
In order to improve the prediction accuracy of building settlement deformation and minimize the impact of non deformation noise component in monitoring data on prediction results, in this paper, singular spectrum analysis is introduced based on Elman neu-ral network model (SSA, single spectrum analysis) method to construct a new SSA-Elman neural network model. Firstly, the SSA method is used to extract the trend component and periodic component in the settlement monitoring data, eliminate the noise compo-nent and improve the signal-to-noise ratio of the monitoring data; secondly, the Elman neural network model is used to predict the trend component and periodic component respectively, and the corresponding component prediction results are obtained; finally the fi-nal prediction result is obtained from the prediction results of structural trend component and periodic component. Through the meas-ured building settlement data, Elman neural network model and SSA-Elman neural network model are modeled and predicted respec-tively. The results show that SSA-Elman neural network model has higher prediction accuracy and is more suitable for long-term pre-diction.
万方数据
维普
