优化神经网络模型在瓦斯涌出预测中的应用
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摘要
为准确预测矿井瓦斯涌出量,降低瓦斯涌出带来的危害,通过灰色关联分析理论得出影响瓦斯涌出量的主要因素为原始瓦斯含量>煤层厚度>煤层埋深>工作面长度>推进速度>煤层倾角,通过优化RBF模型对瓦斯涌出量预测模型进行构建,并运用Matlab仿真模拟预测矿井瓦斯涌出量,结果显示:基于优化RBF模型仿真模拟预测得出的矿井瓦斯涌出量与实际瓦斯涌出量非常接近,5组预测数据中,最大误差为3.6%,最小误差为0.8%,平均误差为1.84%,预测精度较高,可应用于矿井瓦斯涌出量的预测当中。
In order to accurately predict the amount of mine gas emission, reduce the harm caused by gas emission, through gray correlation analysis theory, we indicate that the main factors affecting gas emission is original gas content > thickness of coal seam> depth of coal seam > length of working surface > advancing speed > inclination of coal seam. The prediction model of gas emission is constructed by RBF model optimization, and we use Matlab simulation to predict the mine gas emission. The results show that simulation prediction result based on the RBF model optimization is very close to actual gas emission, in 5 sets of forecast data, the maximum error is 3.6%, the minimum error is 0.8%, the average error is 1.84%. The prediction accuracy is higher and can be applied to the prediction of mine gas emission.
In order to accurately predict the amount of mine gas emission, reduce the harm caused by gas emission, through gray correlation analysis theory, we indicate that the main factors affecting gas emission is original gas content > thickness of coal seam> depth of coal seam > length of working surface > advancing speed > inclination of coal seam. The prediction model of gas emission is constructed by RBF model optimization, and we use Matlab simulation to predict the mine gas emission. The results show that simulation prediction result based on the RBF model optimization is very close to actual gas emission, in 5 sets of forecast data, the maximum error is 3.6%, the minimum error is 0.8%, the average error is 1.84%. The prediction accuracy is higher and can be applied to the prediction of mine gas emission.
引文
[1]米翔繁.基于PCA-BP神经网络的回采工作面瓦斯涌出量预测研究[J].低碳世界,2016(21):71-72.
[2]吴兵,郭志国,王紫薇.基于ARIMA-GM模型的采掘工作面瓦斯涌出预测[J].煤矿安全,2015,46(11):152.
[3]李润求,施式亮,伍爱友,等.采煤工作面瓦斯涌出预测的EMD-Elman方法及应用[J].中国安全科学学报,2014,24(6):51-56.
[4]梁晓珍,王依,侯玮.矿井瓦斯涌出量动态预测数值模拟方法[J].煤炭技术,2012,31(5):69-70.
[5]李振兴,王小研.基于BP神经网络的采煤工作面瓦斯涌出量预测[J].煤炭工程,2016,48(S1):98-100.
[6]张胜军,朱瑞杰,姜春露.基于偏最小二乘回归的回采工作面瓦斯涌出量预测模型[J].煤矿安全,2013,44(2):7-11.
[7]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.
[8]田美智.获各琦铜矿通风系统优化研究[D].赣州:江西理工大学,2012.
[9]裴韶华.基于优化的神经网络模型在矿井瓦斯涌出预测中的应用研究[D].太原:太原理工大学,2014.
[10]姚亚锋,程桦,荣传新,等.基于RBF模糊神经网络模型的深厚冲积层立井冻结压力分析与预测[J].采矿与安全工程学报,2016,33(1):70-76.
[2]吴兵,郭志国,王紫薇.基于ARIMA-GM模型的采掘工作面瓦斯涌出预测[J].煤矿安全,2015,46(11):152.
[3]李润求,施式亮,伍爱友,等.采煤工作面瓦斯涌出预测的EMD-Elman方法及应用[J].中国安全科学学报,2014,24(6):51-56.
[4]梁晓珍,王依,侯玮.矿井瓦斯涌出量动态预测数值模拟方法[J].煤炭技术,2012,31(5):69-70.
[5]李振兴,王小研.基于BP神经网络的采煤工作面瓦斯涌出量预测[J].煤炭工程,2016,48(S1):98-100.
[6]张胜军,朱瑞杰,姜春露.基于偏最小二乘回归的回采工作面瓦斯涌出量预测模型[J].煤矿安全,2013,44(2):7-11.
[7]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.
[8]田美智.获各琦铜矿通风系统优化研究[D].赣州:江西理工大学,2012.
[9]裴韶华.基于优化的神经网络模型在矿井瓦斯涌出预测中的应用研究[D].太原:太原理工大学,2014.
[10]姚亚锋,程桦,荣传新,等.基于RBF模糊神经网络模型的深厚冲积层立井冻结压力分析与预测[J].采矿与安全工程学报,2016,33(1):70-76.