| 基于改进BP神经网络的瓦斯含量预测模型 |
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| 引用本文： | 赵延明.基于改进BP神经网络的瓦斯含量预测模型[J].工矿自动化,2009,35(4). |
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| 作者姓名： | 赵延明 |
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| 作者单位： | 湖南科技大学信息与电气工程学院,湖南,湘潭,411201 |
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| 基金项目： | 湖南省教育厅资助项目(07C265) |
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| 摘 要： | 煤层瓦斯含量是矿井安全生产的重要性能指标之一,而常规基于经验和传统数学模型的预测方法难以准确预测煤层瓦斯含量。针对该问题,文章在分析了基于Fletcher-Reeves共轭梯度法的改进BP神经网络模型的基础上,结合煤层瓦斯含量的各种影响因素,建立了一个基于3层改进BP神经网络的瓦斯含量预测模型,并进行了具体的网络训练和预测仿真。结果表明,该瓦斯含量预测模型收敛速度快,预测精度高,可满足实际生产要求。
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| 关 键 词： | 瓦斯含量 预测 Fletcher-Reeves共轭梯度法 BP神经网络 改进 |
Predicting Model of Gas Content Based on Improved BP Neural Network |
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| ZHAO Yan-ming.Predicting Model of Gas Content Based on Improved BP Neural Network[J].Industry and Automation,2009,35(4). |
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| Authors: | ZHAO Yan-ming |
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| Affiliation: | School of Information and Electrical Engineering of Hunan University of Science and Technology;Xiangtan 411201;China |
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| Abstract: | Gas content in coal seam is one of important performance indexes of safety production in coal mine,but routine predicting methods based on experience and traditional mathematical model are difficult to predict gas content in coal seam accurately.Aiming at the problem,on base of analyzing model of improved BP neural network based on Fletcher-Reeves conjugate gradient method,combining with kinds of influence factors of gas content in coal seam,the paper established a predicting model of gas content based on i... |
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| Keywords: | gas content predict Fletcher-Reeves conjugate gradient method BP neural network (improvement) |
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