小波神经网络在露天矿边坡变形预测中的应用  

Application of wavelet neural network in open-pit mine slope deformation prediction

作　　者：李长洪[1,2] 范丽萍[1,2] 郭俊温[3] 

机构地区：[1]北京科技大学金属矿山高效开采与安全教育部重点实验室,北京100083 [2]北京科技大学土木与环境工程学院,北京100083 [3]内蒙古科技大学,内蒙古包头014010

出　　处：《中国矿业》2010年第7期77-79,共3页China Mining Magazine

基　　金：国家重点基础研究发展规划973项目(编号:2010CB731501;编号:2010CB731506)

摘　　要：为了提高边坡位移变形监测数据预测的精度和可靠性,建立了基于改进BP算法的小波神经网络预测模型。以水厂铁矿GPS边坡监测数据为样本,通过编制Matlab小波神经网络程序进行训练和预测。结果表明,小波神经网络预测模型有良好的函数逼近能力及容错能力。因此,该预测模型在非线性时间序列预测中,具有高精度性和可靠性。improve the accuracy and reliability of the prediction of slope deformation displacement monitoring data,the predictive model of wavelet neural network based on BP algorithm is established.The GPS slope monitoring data in Shuichang iron mine are viewed as samples,and they are trained and predicted through making Matlab program.It indicates that the wavelet neural network predictive model had the more nimble effective approximation of function ability and the strong fault tolerance.With the example tested,the wavelet neural network predictive model have the high-precision and reliability in nonlinear time series prediction.

关 键 词：小波神经网路 边坡变形 变形预测 

分 类 号：TD326[矿业工程—矿井建设]