基于小波神经网络的CFG桩复合地基承载力预测

Prediction of bearing capacity of CFG piles composite foundation based on wavelet neural network

针对BP神经网络学习时间长、收敛速度慢等缺陷,借助小波分析理论,将母小波平移和伸缩构成的小波基作为神经网络的激励函数,通过指导网络的初始化和参数选取,使网络以较简单的拓扑结构实现函数逼近,利用网络训练建立起承载力与其影响因素之间的非线性关系。在相同结构和参数下,与BP神经网络进行分析对比。结果表明:利用小波变换对数据时频局域化分析的能力并结合人工神经网络的自学习功能,使得小波神经网络预测模型具有较强的逼近和容错能力,预测结果比传统的BP神经网络具有更快的收敛速度和更高的精度。

Aiming at the long training time and slow convergence rate of BP neural network, wavelet basis consisted by the translation and stretch of mother wavelet constitutes the activation function of neural network basing on the theory of wavelet analysis. The neural network can use simple topology to approximate function by the guidance of network initialization and parameters selection. The nonlinear relationship between the bearing capacity of CFG piles composite foundation and its main factors is established by using the trained network. Under the same structure and parameters, the prediction results are analyzed and compared with the BP neural network. The result shows that this forecast model makes full use of the wavelet transformation to analysis the data of time - frequency localization and is combined with the self - learning function of artificial neural network,which makes it have a strong ability to approach and fault tolerance. The precision and speed by using the trained wavelet neural network is higher and its predicting result is more accurate than that of BP network.