灰色Elman神经网络的矿井瓦斯涌出量预测  

Prediction of Mine Gas Emission of Gray Elman Neural Network

作　　者：贾花萍[1] 

机构地区：[1]渭南师范学院数学与信息科学学院,陕西渭南714000

出　　处：《计算机技术与发展》2014年第6期236-238,242,共4页Computer Technology and Development

基　　金：陕西省教育专项科研计划项目(11JK0480);陕西省自然科学基础研究计划项目(2011JM1010);渭南师范学院院级项目(12YKS029)

摘　　要：为了准确预测矿井瓦斯涌出量,将灰色理论与Elman神经网络模型结合,建立矿井瓦斯涌出量预测模型。灰色系统能较好地预测变化的趋势,而Elman神经网络具有动态特性好、逼近速度快、精度高等特点。对于煤矿生产中瓦斯涌出量的预测,两者结合能够发挥各自的优势,以某煤矿矿井为例,影响瓦斯涌出量的因素为预测因子建立灰色理论与Elman神经网络融合的预测模型。结果表明,灰色Elman神经网络模型优于传统灰色预测模型,提高了预测精度,达到了很好的预测效果。In order to accurately predict the amount of mine gas emission,the gray theory and Elman neural network model are combined to establish prediction model of mine gas emission.The gray system can predict the trend better,and the Elman neural network has good dynamic performance,quick convergence,high accuracy.For the prediction of gas emission in coal production,combination of the two can play their respective advantages,take a coal mine as an example,influence factors of gas emission as prediction factor to establish forecasting model of gray theory and Elman neural network fusion.The results show that,the gray Elman neural network model is better than the conventional gray forecasting model,improving the prediction accuracy,the prediction effect is very good.

关 键 词：灰色 ELMAN神经网络 瓦斯涌出量 融合 预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]