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变形监测是安全化、信息化工程建设和管理的重要内容,贯穿于建筑物设计、施工和运营整个过程.本文基于小波分析、BP神经网络、小波分析与神经网络结合的相关理论,借助MATLAB编程,建立了改进的BP神经网络、辅助式小波神经网络、嵌入式小波神经网络3种变形预测网络模型.结合工程实测数据,利用建立的3种模型,分别应用累积沉降和期间沉降不同模式数据进行预测.结果表明,两种小波神经网络组合模型的预测效果明显优于单一的BP神经网络模型,具有更高预测精度和更快的收敛速度,且训练样本数目越多,模型精度越高,预测效果越好.
Deformation monitoring is an important part of security and information engineering construction and management, which runs through the whole process of building design, construction and operation.Based on the related theories of wavelet analysis, BP neural network, wavelet analysis and neural network, , Three kinds of deformation prediction network models of BP neural network, auxiliary wavelet neural network and embedded wavelet neural network are established.Combined with the measured data of the project, three models are established, and the data of cumulative settlement and different patterns of subsidence are applied respectively The results show that the prediction results of the two wavelet neural network combined models are obviously better than the single BP neural network model, which has higher prediction accuracy and faster convergence rate. The more the training samples are, the higher the model accuracy is. The better