基于遗传算法-BP神经网络的煤层注水效果分析
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摘要
为了提高BP神经网络预测煤层注水效果的精度,采用遗传算法优化BP神经网络的权值和阈值,建立了遗传算法-BP神经网络模型,并采用该模型对煤层注水湿润半径进行模拟预测。Matlab模拟结果表明,遗传算法-BP神经网络模型的预测结果比BP神经网络模型更准确,平均相对误差降低了40.29%,训练步数减少了1 665步,收敛速度快,稳定性好。
In order to improve prediction accuracy of coal seam water infusion effect by using BP neural network,genetic algorithm was used to optimize weight value and threshold value of BP neural network.Genetic algorithm-BP neural network model was built and used to predict wetting radius of coal seam water infusion.The Matlab simulation result shows that the genetic algorithm-BP neural network model has more accurate prediction result than BP neural network model,average relative error of the genetic algorithm-BP neural network model is reduced by 40.29%,training steps are reduced by 1 665 steps,convergence speed is fast and stability is good.
In order to improve prediction accuracy of coal seam water infusion effect by using BP neural network,genetic algorithm was used to optimize weight value and threshold value of BP neural network.Genetic algorithm-BP neural network model was built and used to predict wetting radius of coal seam water infusion.The Matlab simulation result shows that the genetic algorithm-BP neural network model has more accurate prediction result than BP neural network model,average relative error of the genetic algorithm-BP neural network model is reduced by 40.29%,training steps are reduced by 1 665 steps,convergence speed is fast and stability is good.
引文
[1]李丽丽.煤层注水效果分析的仿真研究[J].计算机仿真,2012,29(4):234-237.
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[5]胡锦宏.基于遗传神经网络的煤层气含量预测[J].绿色科技,2015(2):260-263.
[6]吉振光.基于遗传算法改进的BP神经网络模型在邻近层瓦斯涌出量预测中的应用[J].煤矿安全,2011,42(7):36-38.
[7]刘奕君,赵强,郝文利.基于遗传算法优化BP神经网络的瓦斯浓度预测研究[J].矿业安全与环保,2015,42(2):56-60.
[8]陈俊杰,王明远.基于遗传算法的拐点偏移距神经网络预测模型构建及分析[J].中国地质灾害与防治学报,2015,26(2):142-147.
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[10]张红鸽,张钊,张伟.遗传算法优化BP神经网络预测上覆岩层破坏范围[J].煤矿安全,2012,43(4):15-17.
[11]金龙哲,李晋平,孙玉福,等.矿井粉尘防治理论[M].北京:科学出版社,2010:67-71.
[12]秦书玉,张永吉,田利军,等.煤矿安全数学分析与预测[M].北京:煤炭工业出版社,2002:271.
[13]宁齐元,刘祖德,游曦鸣,等.基于BP神经网络煤与瓦斯突出强度预测模型[J].煤矿开采,2012,16(6):19-20.
[2]顾新宇,马尚权,陈开岩,等.有限元方法模拟分析煤层注水效果[J].煤炭技术,2010,29(6):106-108.
[3]祖海军.基于FLUENT的煤层注水湿润半径模拟研究[J].煤炭与化工,2014,37(1):124-127.
[4]施伟,刘建辉,秦书玉.基于BP网络的煤层注水效果的预测[J].计算机仿真,2009,26(3):172-174.
[5]胡锦宏.基于遗传神经网络的煤层气含量预测[J].绿色科技,2015(2):260-263.
[6]吉振光.基于遗传算法改进的BP神经网络模型在邻近层瓦斯涌出量预测中的应用[J].煤矿安全,2011,42(7):36-38.
[7]刘奕君,赵强,郝文利.基于遗传算法优化BP神经网络的瓦斯浓度预测研究[J].矿业安全与环保,2015,42(2):56-60.
[8]陈俊杰,王明远.基于遗传算法的拐点偏移距神经网络预测模型构建及分析[J].中国地质灾害与防治学报,2015,26(2):142-147.
[9]雷英杰,张善文.MATLAB遗传算法工具箱及其应用[M].西安:西安电子科技大学出版社,2005:8-9.
[10]张红鸽,张钊,张伟.遗传算法优化BP神经网络预测上覆岩层破坏范围[J].煤矿安全,2012,43(4):15-17.
[11]金龙哲,李晋平,孙玉福,等.矿井粉尘防治理论[M].北京:科学出版社,2010:67-71.
[12]秦书玉,张永吉,田利军,等.煤矿安全数学分析与预测[M].北京:煤炭工业出版社,2002:271.
[13]宁齐元,刘祖德,游曦鸣,等.基于BP神经网络煤与瓦斯突出强度预测模型[J].煤矿开采,2012,16(6):19-20.