回采工作面瓦斯涌出量VMD-DE-RVM区间预测方法
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摘要
为有效、准确地预测回采工作面绝对瓦斯涌出量,基于变分模态分解(VMD)方法;差分进化(DE)算法和相关向量机(RVM)原理,提出回采工作面绝对瓦斯涌出量的VMD-DE-RVM区间预测方法;通过VMD方法将绝对瓦斯涌出量分解为若干固有模态分量并分析其局部特征,分别建立每个固有模态分量的RVM预测模型,并通过DE算法优化模型参数以提高预测精度;加权叠加各个分量的预测结果得到绝对瓦斯涌出量预测结果,并将其与经验模态分解方法所得结果对比。结果表明:应用该方法预测回采工作面瓦斯涌出量,能弱化瓦斯涌出量的局部特征,得到置信度为95%时涌出量预测区间有效度为100%,平均绝对误差为0. 096 m3/min,平均相对误差为2. 43%,预测精度有所提高。
In order to effectively and accurately predict the absolute gas emission of the mining face,a VMD-DE-RVM interval prediction method was worked out for absolute gas emission from a mining face based on VMD method,DE algorithm and RVM principle. For working out the method,the absolute gas emission was decomposed into several intrinsic mode components by VMD method and the local characteristics were analyzed. RVM prediction models were built for the intrinsic mode component,and themodel parameters were optimized by DE algorithm to improve the prediction accuracy. Prediction results of the components were weighted and superimposed to obtain an absolute gas emission prediction result. A prediction result comparison was made between the three models. The results show that the method worked out by the authors can weaken the local characteristics of gas emission,and the prediction interval effectiveness is 100%,the average absolute error is 0. 096 m3/min and the average relative error is 2. 43%when the confidence is 95%,and the prediction accuracy is improved.
In order to effectively and accurately predict the absolute gas emission of the mining face,a VMD-DE-RVM interval prediction method was worked out for absolute gas emission from a mining face based on VMD method,DE algorithm and RVM principle. For working out the method,the absolute gas emission was decomposed into several intrinsic mode components by VMD method and the local characteristics were analyzed. RVM prediction models were built for the intrinsic mode component,and themodel parameters were optimized by DE algorithm to improve the prediction accuracy. Prediction results of the components were weighted and superimposed to obtain an absolute gas emission prediction result. A prediction result comparison was made between the three models. The results show that the method worked out by the authors can weaken the local characteristics of gas emission,and the prediction interval effectiveness is 100%,the average absolute error is 0. 096 m3/min and the average relative error is 2. 43%when the confidence is 95%,and the prediction accuracy is improved.
引文
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[14] STORN R,PRICE K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of global optimization,1997,11(4):341-359.
[15]陈涛,雍龙泉,邓方安,等.基于差分进化算法的支持向量机参数选择[J].计算机工程与应用,2011,47(5):24-26.CHEN Tao,YONG Longquan,DENG Fang'an,et al. Parameters selection of support vector machine based on differential evolution[J]. Computer Engineering and Applications,2011,47(5):24-26.
[2]黄文标,施式亮.基于改进Lyapunov指数的瓦斯涌出时间序列预测[J].煤炭学报,2009,34(12):1 665-1 668.HUANG Wenbiao,SHI Shiliang. Predicting on the time series of gas emission based on improved Lyapunov index[J].Journal of China Coal Society,2009,34(12):1 665-1 668.
[3]董晓雷,贾进章,白洋,等.基于SVM耦合遗传算法的回采工作面瓦斯涌出量预测[J].安全与环境学报,2016,16(2):114-118.DONG Xiaolei,JIA Jinzhang,BAI Yang,et al. Prediction for gas-gushing amount from the working face of stope based on the SVM coupling genetic algorithm[J]. Journal of Safety and Environment,2016,16(2):114-118.
[4]杨武艳,郁钟铭. GM(1,1)灰色预测模型在矿井瓦斯涌出量预测中的应用[J].矿业工程研究,2012,27(4):46-49.YANG Wuyan,YU Zhongming. Application of GM(1,1)grey forecasting model in mine gas emission prediction[J].Mineral Engineering Research,2012,27(4):46-49.
[5]付华,代巍.基于LLE与BA-Elman的瓦斯涌出量动态预测研究[J].传感技术学报,2016,29(9):1 383-1 388.FU Hua,DAI Wei. Dynamic prediction of gas emission based on LLE and BA-ENN[J]. Chinese Journal of Sensors and Actuators,2016,29(9):1 383-1 388.
[6]温廷新,孙雪,孔祥博.基于PSOBP-AdaBoost模型的瓦斯涌出量分源预测研究[J].中国安全科学学报,2016,26(5):94-98.WEN Tingxin,SUN Xue,KONG Xiangbo,et al. Research on prediction of gas emission quantity with sub sources basing on PSOBP-Ada Boost[J]. China Safety Science Journal,2016,26(5):94-98.
[7]撒占友,刘岩,刘杰.基于EMD-ARMA的矿井瓦斯涌出量预测[J].煤矿安全,2016,47(7):174-176,181.SA Zhanyou,LIU Yan,LIU Jie. Mine gas emission prediction based on EMD-ARMA model[J]. Safety in Coal Mines,2016,47(7):174-176,181.
[8]卢国斌,李晓宇,祖秉辉,等.基于EMD-MFOA-ELM的瓦斯涌出量时变序列预测研究[J].中国安全生产科学技术,2017,13(6):109-114.LU Guobin,LI Xiaoyu,ZU Binghui,et al. Research on time-varying series forecasting of gas emission quantity based on EMD-MFOA-ELM[J]. Journal of Safety Science and Technology,2017,13(6):109-114.
[9]施式亮,李润求,罗文柯.基于EMD-PSO-SVM的煤矿瓦斯涌出量预测方法及应用[J].中国安全科学学报,2014,24(7):43-49.SHI Shiliang,LI Runqiu,LUO Wenke. Method for predicting coal mine gas emission based on EMD-PSO-SVM and its application[J]. China Safety Science Journal,2014,24(7):43-49.
[10] DRAGOMIRETSKIY K,ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing,2014,62(3):531-544.
[11]唐贵基,王晓龙.参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用[J].西安交通大学学报,2015,49(5):73-81.TANG Guiji,WANG Xiaolong. Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing[J]. Journal of Xi'an Jiaotong University,2015,49(5):73-81.
[12] TIPPING M. Relevance vector machine:U.S. Patent 6,633,857[P]. 2003-10-14.
[13]春唐琪,包腾飞,杜传阳,等.逐步RVM-Markov模型在大坝变形预测中的应用[J].三峡大学学报:自然科学版,2015,37(6):47-51.CHUN Tangqi,BAO Tengfei,DU Chuanyang,et al. Application of progressively RVM-Markov model to prediction of dam deformations[J]. Journal of China Three Gorges University:Natural Sciences,2015,37(6):47-51.
[14] STORN R,PRICE K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of global optimization,1997,11(4):341-359.
[15]陈涛,雍龙泉,邓方安,等.基于差分进化算法的支持向量机参数选择[J].计算机工程与应用,2011,47(5):24-26.CHEN Tao,YONG Longquan,DENG Fang'an,et al. Parameters selection of support vector machine based on differential evolution[J]. Computer Engineering and Applications,2011,47(5):24-26.
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