| 基于灰色神经网络预测潘一东矿瓦斯含量 |
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| 引用本文： | 沈金山,王来斌,许继影,高锡擎,郑飞.基于灰色神经网络预测潘一东矿瓦斯含量[J].煤炭技术,2011,30(4). |
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| 作者姓名： | 沈金山 王来斌 许继影 高锡擎 郑飞 |
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| 作者单位： | 安徽理工大学,地球与环境学院,安徽,淮南,232001 |
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| 基金项目： | 安徽省教育厅自然基金(2006kj002B) |
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| 摘 要： | 运用灰色关联分析影响潘一东矿井瓦斯含量的各因素,得出煤层标高、顶板岩性、煤厚、地质构造是影响瓦斯赋存的主要因素。选取这四种因素作为神经网络的神经元进行建模预测,结果表明,基于灰色关联度的神经网络模型预测瓦斯含量,预测精度高,证明了基于灰色理论与神经网络预测模型的可靠性。
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| 关 键 词： | 瓦斯含量 灰色关联度 神经网络 |
Gas Content Prediction of Panyi East Coal Mine Based on Gray Neural Network |
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| SHEN Jin-shan,WANG Lai-bin,XU Ji-ying,GAO Xi-qing,ZHENG Fei.Gas Content Prediction of Panyi East Coal Mine Based on Gray Neural Network[J].Coal Technology,2011,30(4). |
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| Authors: | SHEN Jin-shan WANG Lai-bin XU Ji-ying GAO Xi-qing ZHENG Fei |
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| Affiliation: | SHEN Jin-shan,WANG Lai-bin,XU Ji-ying,GAO Xi-qing,ZHENG Fei(School of Earth and Environment,Anhui University of Science and Technology,Huainan 232001,China) |
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| Abstract: | This paper analyses the factors affect gas content of Panyi East Coal Mine with grey relationship analysis method,the research results show that seam elevation,roof lithology,coal thickness and geological structure are the main factors that affect gas occurrence situation.The paper selects these four factors as neurons of neural network to build model and predict,the results reveal that neural network model based on grey relational grade has a high precision of predicting gas content,which certify the relia... |
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| Keywords: | gas content grey relational grade neural network |
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