基于灰色关联熵的煤与瓦斯突出概率神经网络预测模型
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摘要
煤与瓦斯突出是严重威胁矿井安全生产的重大自然灾害之一。为解决煤与瓦斯突出影响因素、突出危险性关联预测问题,在综合分析煤与瓦斯突出影响因素的基础上,利用灰色关联熵理论分析影响因素与突出危险性的关联度,得到各影响因素的权重及关联度排序,并结合概率神经网络(PNN)原理,构建基于灰色关联熵的煤与瓦斯突出PNN预测模型。用煤与瓦斯突出样本数据,对影响因素加权的PNN模型进行训练和测试。结果表明,用灰色关联熵分析可获得影响因素与突出危险性的关系,量化输入变量的重要性;瓦斯放散初速度、开采深度对于煤与瓦斯突出危险性的影响程度最大,可重点对瓦斯放散初速度、开采深度进行预处理以产生更为理想的预测效果;该预测模型能更好地考虑影响因素对突出危险性的综合影响,改善煤与瓦斯突出危险性预测的准确性。
Coal and gas outburst was one of the most serious natural disasters which threatened the safety production of coal mine. In order to solve prediction problem in influential factors of coal and gas outburst associated relationships with the outburst risk,based on a comprehensive analysis of influence factors of coal and gas outburst,this paper used grey relational entropy theory to analysis correlation of factors with outburst risk,got the weight and correlation order of influencing factors,and combined with principle of probabilistic neural network( PNN). It built PNN prediction model of coal and gas outburst based on grey correlation entropy. Using the sample data of coal and gas outburst,it trained and tested the PNN model of influencing factors weighted. The results show that the relationship between influence factors and outburst risk by using grey correlation entropy analysis is obtained,and the importance of input variables is quantified; initial speed of methane diffusion and mining depth have maximum relevance to coal and gas outburst risk,and preprocessing of initial speed of methane emission,mining depth can be focused to produce more ideal prediction effect; the prediction model can better consider that influencing factors have a comprehensive effect on outburst risk,and improve the accuracy of risk prediction of coal and gas outburst.
Coal and gas outburst was one of the most serious natural disasters which threatened the safety production of coal mine. In order to solve prediction problem in influential factors of coal and gas outburst associated relationships with the outburst risk,based on a comprehensive analysis of influence factors of coal and gas outburst,this paper used grey relational entropy theory to analysis correlation of factors with outburst risk,got the weight and correlation order of influencing factors,and combined with principle of probabilistic neural network( PNN). It built PNN prediction model of coal and gas outburst based on grey correlation entropy. Using the sample data of coal and gas outburst,it trained and tested the PNN model of influencing factors weighted. The results show that the relationship between influence factors and outburst risk by using grey correlation entropy analysis is obtained,and the importance of input variables is quantified; initial speed of methane diffusion and mining depth have maximum relevance to coal and gas outburst risk,and preprocessing of initial speed of methane emission,mining depth can be focused to produce more ideal prediction effect; the prediction model can better consider that influencing factors have a comprehensive effect on outburst risk,and improve the accuracy of risk prediction of coal and gas outburst.
引文
[1]高卫东. Fisher判别法在煤与瓦斯突出危险程度预测中的应用[J].中国安全科学学报,2010,20(10):26-30.
[2]付华,李海霞,卢万杰,等.一种改进的极限学习机煤与瓦斯突出预测模型[J].传感技术学报,2016,29(1):69-74.
[3]温廷新,孙红娟,张波,等.煤与瓦斯突出预测的QGA-LSSVM模型[J].中国安全生产科学技术,2015,11(5):5-12.
[4]候三中.瓦斯参数变化对钻孔瓦斯涌出初速度的影响[J].煤矿现代化,2016(1):119-121.
[5]崔大尉.基于地震信息的煤与瓦斯突出预测与评价方法研究[D].徐州:中国矿业大学,2015.
[6]李杰.煤与瓦斯突出IGSA-SVM预测模型及其应用[D].太原:太原理工大学,2016.
[7]孙玉峰,李中才.支持向量机法在煤与瓦斯突出分析中的应用研究[J].中国安全科学学报,2010,20(1):25-30.
[8]毕建武,贾进章,赵博琦,等.煤与瓦斯突出预测的Fisher判别分析法及应用[J].安全与环境学报,2014,14(5):57-60.
[9]念其锋,施式亮,李润求.基于网络分析和联系熵的煤与瓦斯突出预测研究[J].中国安全生产科学技术,2014,10(2):22-27.
[10]靳玉萍,李保霖.基于遗传优化径向基概率神经网络的岩性识别应用[J].计算机应用,2013,33(2):353-356.
[11]庞素琳.概率神经网络信用评价模型及预警研究[J].系统工程理论与实践,2005,25(5):3-48.
[12]念其锋,施式亮,李润求,等.基于PNN的煤矿安全生产风险综合预警研究[J].中国安全生产科学技术,2013,9(10):71-77.
[13]邵良杉,徐波.煤层底板突水危险性的PNN预测模型研究及应用[J].中国安全科学学报,2015,25(8):93-98.
[14]杨力,刘程程,宋利,等.基于熵权法的煤矿应急救援能力评价[J].软科学研究成果与动态,2013(11):185-192.
[15]张文泉,赵凯,张贵彬,等.基于灰色关联度分析理论的底板破坏深度预测[J].煤炭学报,2015,40(S1):53-59.
[16]肖韬,袁兴中,唐清华,等.基于概率神经网络的城市湖泊生态系统健康评价研究[J].环境科学学报,2013,33(11):3166-3172.
[17]付小平,薛新华,李洪涛. GRNN模型在煤与瓦斯突出及瓦斯含量预测中的应用[J].中国安全科学学报,2012,22(1):24-28.
[18]张瑾.基于优化支持向量机的煤与瓦斯突出预测研究[D].阜新:辽宁工程技术大学,2015.
[2]付华,李海霞,卢万杰,等.一种改进的极限学习机煤与瓦斯突出预测模型[J].传感技术学报,2016,29(1):69-74.
[3]温廷新,孙红娟,张波,等.煤与瓦斯突出预测的QGA-LSSVM模型[J].中国安全生产科学技术,2015,11(5):5-12.
[4]候三中.瓦斯参数变化对钻孔瓦斯涌出初速度的影响[J].煤矿现代化,2016(1):119-121.
[5]崔大尉.基于地震信息的煤与瓦斯突出预测与评价方法研究[D].徐州:中国矿业大学,2015.
[6]李杰.煤与瓦斯突出IGSA-SVM预测模型及其应用[D].太原:太原理工大学,2016.
[7]孙玉峰,李中才.支持向量机法在煤与瓦斯突出分析中的应用研究[J].中国安全科学学报,2010,20(1):25-30.
[8]毕建武,贾进章,赵博琦,等.煤与瓦斯突出预测的Fisher判别分析法及应用[J].安全与环境学报,2014,14(5):57-60.
[9]念其锋,施式亮,李润求.基于网络分析和联系熵的煤与瓦斯突出预测研究[J].中国安全生产科学技术,2014,10(2):22-27.
[10]靳玉萍,李保霖.基于遗传优化径向基概率神经网络的岩性识别应用[J].计算机应用,2013,33(2):353-356.
[11]庞素琳.概率神经网络信用评价模型及预警研究[J].系统工程理论与实践,2005,25(5):3-48.
[12]念其锋,施式亮,李润求,等.基于PNN的煤矿安全生产风险综合预警研究[J].中国安全生产科学技术,2013,9(10):71-77.
[13]邵良杉,徐波.煤层底板突水危险性的PNN预测模型研究及应用[J].中国安全科学学报,2015,25(8):93-98.
[14]杨力,刘程程,宋利,等.基于熵权法的煤矿应急救援能力评价[J].软科学研究成果与动态,2013(11):185-192.
[15]张文泉,赵凯,张贵彬,等.基于灰色关联度分析理论的底板破坏深度预测[J].煤炭学报,2015,40(S1):53-59.
[16]肖韬,袁兴中,唐清华,等.基于概率神经网络的城市湖泊生态系统健康评价研究[J].环境科学学报,2013,33(11):3166-3172.
[17]付小平,薛新华,李洪涛. GRNN模型在煤与瓦斯突出及瓦斯含量预测中的应用[J].中国安全科学学报,2012,22(1):24-28.
[18]张瑾.基于优化支持向量机的煤与瓦斯突出预测研究[D].阜新:辽宁工程技术大学,2015.
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