基于LLE-FOA-BP模型的煤与瓦斯突出强度预测
Prediction of coal and gas outburst intensity based on LLE-FOA-BP model
【索引】隆能增,袁梅,敖选俊,等.基于LLE-FOA-BP模型的煤与瓦斯突出强度预测[J].工矿qy288千亿国际,2019,45(10):68-73.
【Reference】LONG Nengzeng,YUAN Mei,AO Xuanjun,et al.Prediction of coal and gas outburst intensity based on LLE-FOA-BP model[J].Industry and Mine Automation,2019,45(10):68-73.
【DOI】10.13272/j.issn.1671-251x.2019010054
【作者】隆能增1,袁梅1,2,3,4,敖选俊5,李鑫灵1,张平1
【Author】 LONG Nengzeng1,YUAN Mei1,2,3,4,AO Xuanjun5,LI Xinling1,ZHANG Ping1
【作者机构】1.贵州大学 矿业学院, 贵州 贵阳550025;2.贵州省非金属矿产资源综合利用重点实验室, 贵州 贵阳550025;3.贵州省优势矿产资源高效利用工程实验室, 贵州 贵阳550025;4.复杂地质矿山开采安全技术工程中心,贵州 贵阳550025;5.贵州中纸投资有限公司,贵州 盘州553537
【Unit】1.Mining College, Guizhou University, Guiyang 550025, China; 2.Guizhou Key Laboratory of Comprehensive Utilization of Non-metallic Mineral Resources, Guiyang 550025, China;3.Guizhou Engineering Lab of Advantage Mineral Resources Efficient Utilization, Guiyang 550025, China; 4.Engineering Center for Safe Mining Technology under Complex Geologic Conditions, Guiyang 550025, China;5.Guizhou Zhongzhi Investment Co., Ltd., Panzhou 553537, China
【摘要】针对目前煤与瓦斯突出强度预测精度低、稳定性差及训练速度慢等问题,提出了一种基于局部线性嵌入法-果蝇优化算法-BP神经网络(LLE-FOA-BP)模型的煤与瓦斯突出强度预测方法。借助LLE算法的非线性数据特征提取优势,提取煤与瓦斯突出影响因素原始数据的本质特征,形成重构有效因子,降低数据间的冗余信息及噪声;利用FOA算法较强的全局寻优能力优化BP神经网络的权值和阈值,避免陷入局部极小,提高参数寻优效率;将重构有效因子输入优化后的BP神经网络进行训练,实现煤与瓦斯突出强度快速、准确预测。测试结果表明,LLE-FOA-BP模型的平均相对误差为8.06%,相对误差的方差为3.69,经过24次迭代训练就达到10-8的训练精度,能够在保证预测精度的基础上,提高鲁棒性和学习效率。
【Abstract】In view of problems of low prediction accuracy, poor stability and slow training of current coal and gas outburst intensity prediction, a prediction method of coal and gas outburst intensity based on LLE-FOA-BP model was proposed. Essential characteristics of raw data of coal and gas outburst influencing factors are extracted taking use of the advantage of nonlinear data feature extraction of LLE algorithm, effective reconstruction factors are formed, and redundant information and noise between data are reduced. The weight and threshold of BP neural network are optimized by using FOA algorithm's strong global optimization ability to avoid falling into local minima and improve parameter optimization efficiency. Effective reconstruction factors are input into the optimized BP neural network for training, so as to realize quick and accurate prediction of coal and gas outburst intensity.The test results show that the average relative error of LLE-FOA-BP model is 8.06%, variance of relative error is 3.69, and training accuracy of 10-8 is achieved after 24 iterations, which verifies the model can improve robustness and learning efficiency while ensuring prediction accuracy.
【关键词】 煤与瓦斯突出强度预测; 局部线性嵌入; 果蝇算法; BP神经网络; 大数据处理
【Keywords】prediction of coal and gas outburst intensity; local linear embedding; fruit fly optimization algorithm; BP neural network; big date process
【文献出处】工矿qy288千亿国际,2019年10期
【基金】贵州省科技计划项目(黔科合支撑[2018]2789)
【分类号】TD713
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