人工神经网络在煤矿开采沉陷预计中的应用研究

Research on Application of Artificial Neural Network in Predicting Mining Subsidence

提出了利用人工神经网络技术进行开采沉陷定量预测的新方法 .研究了影响因素的选取、开采沉陷预计模型的建立以及模型的应用等问题 .采用 BP神经网络算法对开采沉陷量进行了建模和预测 .结果表明 ,用神经网络模型对复杂的开采沉陷系统进行模拟预测 ,具有理论上的可行性和现实意义 。

A new method was put forward for the quantitative prediction of mining subsidence by means of ANN (Artificial Neural Network). Problems of selecting influential factors, establishment of ANN prediction model and its application were discussed. BP algorithm was used for modeling and predicting the mining subsidence. Result shows that the ANN prediction model is theoretically feasible and significant in predicting complex exploitation sink system.