摘要
利用矿井涌水量实测值建立灰色理论与神经网络串联组合的预测模型,即利用不同灰色模型预测值训练神经网络进行预测,提高矿井涌水量的预测精度,先后建立了GM(1,1)、二次参数拟合GM(1,1)模型,将其与BP神经网络模型串联形成最终预测模型,以淮南矿区潘三矿西翼矿井涌水量预测为例,结果说明了该模型具有较高的准确性。
This paper establishes forecast model of grey theory and neural network inseries peg by mine hy-draulic discharge measured value - predicts by different predicted value of grey model training neural network to make the forecast of mine hydraulic discharge more accurate. Model GM （1.1） and ting GM （ 1.1 ） are made up one after another, which are connected with BP neural make up the final forecast model. The Xiyi Mine hydraulic discharge of Pansan Mine that this kind of model has high accuracy.
出处
《淮南职业技术学院学报》
2009年第4期25-27,共3页
Journal of Huainan Vocational Technical College
