摘要
介绍了矿山地下开采引起地表下沉对地表建筑设施造成了的损害,分析了地表沉陷受地质条件和采矿条件等诸多因素的影响,且因素之间存在非线性关系,难以用数学模型加以描述,因而求解困难。研究利用神经网络系统对地表沉陷问题进行预测,通过试验采集的数据对神经网络进行了训练和检测,获得了比较满意的结果,预测值和期望输出值之间存在着极小的误差,从而证明了对地表沉陷预测的可行性和实用性。
Underground mining causes surface subsiding, resulting in damage of surface building Surface subsidence is limited by many factors such as geologic conditions and mining conditions. There are nonlinear relations between the factors and it is difficult to describe it by a mathematical model, therefore, there are difficulties in solving some problems. The surface subsidence is predicted by the neural network system. The system is trained and tested by the data collected in tests. It has achieved satisfacto- ry results, and there are very small errors between the predicted and the expected values. The results prove that it is feasible to use neural network to predict the surface subsidence.
出处
《现代矿业》
CAS
2009年第5期66-69,共4页
Modern Mining
关键词
B^P神经网络
地表沉陷预测
矿山开采
Back-propagation neural network
Prediction of surface subsidence
Mine mining
