基于BP神经网络的岩移监测站缺失点数据插补

Study on Data Supplement of Loss Point of Strata Movement Monitoring Station which Base on BP Neural Network

岩层移动监测是研究开采沉陷问题最有效的方法。由于监测时间长、地表积水、人为损坏等原因,监测点极易缺失。研究缺失监测点的数据插补模型对提高岩移监测数据的利用率、总结开采沉陷规律等都具有重要意义。提出了采用BP神经网络模型进行缺失监测点的数据插补的思路,并采用Matlab实现了该模型。研究结果表明,人工神经网络模型能够很好的逼近地表沉陷盆地,用此模型作为岩移监测点数据插补是完全可行的。

Strata movement monitoring is an effective way to study the problem Monitoring point are easily to be lost because of long-time monitoring work ,surface and so on. The study on data supplement model of loss monitoring point has great of mining subsidence. pond,artificial damage significance for improving the utilization ratio of strata movement monitoring data,summarize the mining subsidence law and so on. Thought of using BP neural network model is proposed to work the problem of data supplement of loss monitoring point and realized this model by using Matlab. Research result prove artificial neural network model could approach ground subsidence basin well, and it＇s totally feasible by using this model as the data supplement of strata movement monitoring point.