小波神经网络在桥梁变形预测中的应用

The application of wavelet neural network in the bridge deformation prediction

针对BP网络容易导致局部极小、不收敛的问题,提出了用小波神经网络拟合并预测大桥位移与其原因之间非线性关系的方法。提出了小波神经网络隐含层节点数的确定方法,该法可以确定网络隐含层最优节点数;小波神经网络具有良好的局部特性、较强的学习能力和任意函数逼近能力,实现了大桥变形的精确拟合及预测。实测结果表明:所提算法经过训练不仅可以准确拟合大桥位移曲线,而且预测精度较高,各项指标均优于BP网络。

According to the fact that BP network could easily lead to local minimum and no convergence problem,this paper presented that using wavelet neural network combined prediction method of the nonlinear relationship between displacement of the bridge and its reasons.It was discussed that the method for determining the initial weights.The wavelet neural network had good local characteristics,strong ability of learning and arbitrary function approximation ability and realized accurate fitting and forecasting the deformation of the bridge.The results of experiment indicated that the algorithm trained not only could accurate displacement curve fitting of the bridge,prediction accuracy was higher,and the indicators were better than BP network.This paper presented a method for determining the number of hidden layer nodes of wavelet neural network,and it could determine the optimal network hidden layer nodes number.