摘要
针对边坡变形量预测难的问题,将小波分析与BP神经网络预测相结合,采用小波变换对边坡变形监测数据进行信噪分离,进而消除观测误差,通过BP神经网络预测模型BPANN对处理后数据进行再处理,对边坡变形量以及变形趋势进行预测。进而提出了一种基于小波变换和BPANN模型对露天矿边坡变形监测数据进行处理分析的方法,并在鞍山某露天矿进行了实际应用。实例结果表明:利用小波去噪与BPANN模型预测的监测点精度达到3 mm,满足二等变形监测的要求,数据处理简便,在露天矿边坡变形监测数据的消噪与预测中具有实际应用价值。
In view of difficulty in slope deformation forecast, a data analysis method was put forward based on the combination of wavelet analysis and BP artificial neural network forecast. Wavelet transform was firstly adopted to do signal-noise separation for slope deformation monitoring data so as to eliminate observation error. Then, BP artificial neural network model （BPANN） was used in post-processing. The slope deformation in open-pit mine and its trend were therefore forecasted. The practical application of this method in an Anshan open-pit mine indicates that wavelet denoising combined with BPANN model can predicts the monitoring point with the precision of 3 mm, up to the requirement for the second-class deformation monitoring. It is of practical value to apply such method into denoising of monitoring data and predication of open-pit slope deformation, due to simplicity of data processing.
出处
《矿冶工程》
CAS
CSCD
北大核心
2013年第6期1-5,共5页
Mining and Metallurgical Engineering
基金
国家自然科学基金项目基金(41104104)
关键词
露天矿
小波变换
BPANN(反传人工神经网络)
边坡变形
变形预测
精度分析
open-pit mine
wavelet transform
BPANN （back propagation artificial neural network ）
slope deformation
deformation forecast
precision analysis
