[1]师超,凡永鹏,王延生.SFLA-Verhulst组合模型预测矿井瓦斯涌出量[J].中国安全生产科学技术,2018,14(3):72-76.[doi:10.11731/j.issn.1673-193x.2018.03.010]
SHI Chao,FAN Yongpeng,WANG Yansheng.Prediction of gas emission in mine by SFLA-Verhulst combined model[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(3):72-76.[doi:10.11731/j.issn.1673-193x.2018.03.010]
点击复制
SFLA-Verhulst组合模型预测矿井瓦斯涌出量
SHI Chao,FAN Yongpeng,WANG Yansheng.Prediction of gas emission in mine by SFLA-Verhulst combined model[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(3):72-76.[doi:10.11731/j.issn.1673-193x.2018.03.010]
《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]
- 卷:
- 14
- 期数:
- 2018年3期
- 页码:
- 72-76
- 栏目:
- 职业安全卫生管理与技术
- 出版日期:
- 2018-03-31
文章信息/Info
- Title:
- Prediction of gas emission in mine by SFLA-Verhulst combined model
- 文章编号:
- 1673-193X(2018)-03-0072-05
- 关键词:
- 瓦斯涌出量; Verhulst模型; 混合蛙跳算法; 组合模型; 模型检验
- Keywords:
- gas emission; Verhulst model; shuffled frog leaping algorithm (SFLA); combined model; model verification
- 分类号:
- X937
- 文献标志码:
- A
- 摘要:
- 针对矿井瓦斯涌出量的时变性、波动性、非线性以及不确定性等特征，提出了SFLA-Verhulst组合预测模型，用于对具有非线性动态特征的瓦斯涌出量进行预测。该模型通过蛙跳算法对Verhulst模型的背景值参数寻优，并引入一次指数平滑法对原始数据进行优化处理，建立了基于混合蛙跳算法的SFLA-Verhulst组合预测模型；结果使模型在原始数据不准确或存在误差干扰的情况下仍能进行精度较高的预测。将新模型应用于某矿瓦斯涌出量预测，并对模型的预测结果进行检验分析，结果表明:该模型在结合蛙跳算法的全局寻优特点后预测精度较传统的GM(1,1)模型有明显的提高，适用性更强。
- Abstract:
- In view of the characteristics of gas emission in mine, such as the time-varying, volatility, non-linear and uncertainty, a SFLA-Verhulst combined prediction model was put forward to predict the gas emission with the characteristics of non-linear and dynamic. In this model, the background value parameters of Verhulst model were optimized through the frog leaping algorithm, and the optimizing processing of the raw data was carried out by introducing into the single exponential smoothing method, then the SFLA-Verhulst combined prediction model based on the shuffled frog leaping algorithm (SFLA) was established. It showed that the model could still obtain the prediction results with a higher accuracy when the raw data were not accurate or the error interference existed. The new model was applied to the prediction of gas emission in a certain mine, and the prediction results of the model were verified. The results showed that after combining with the characteristics of global optimization of the frog leaping algorithm, the prediction accuracy of this model was much higher than that of traditional GM (1,1) model, with a greater applicability.
参考文献/References:
［1］孙庆刚. 中国煤矿瓦斯灾害现状与防治对策研究［J］.中国煤炭,2014,40(3):116-119.
SUN Qinggang. China Coal Mine gas disaster situation and measures to combat research ［J］. China Coal ,2014,40(3):116-119.
［2］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.GM (1,1)grey forecasting model in the application of mine gas emission prediction ［J］. Journal of Mining Engineering Research, 2012, 27 (4):46-49.
［3］权国林,赵琳琳,邵良杉. 基于主成分灰关联的瓦斯涌出量预测模型［J］. 辽宁工程技术大学学报(自然科学版)2017,36(4):366-370.
QUAN Guolin,ZHAO Linlin,SHAO Liangshan. Based on the principal component grey relation of gas emission prediction model ［J］. Journal of Liao Ning Engineering Technology University (Natural Science Edition),2017,36(4):366-370.
［4］徐青伟,王兆丰. 瓦斯涌出量预测的GM(1,1)模型改进［J］. 煤炭技术,2015,34(1):147-149.
XU Qingwei,WANG Zhaofeng. Gas emission prediction GM (1,1)model to improve［J］. Journal of Coal Technology,2015 (1):147-149.
［5］陈伟华,闫孝姮,付华. 改进的Elman神经网络在瓦斯涌出量预测中的应用［J］.安全与环境学报,2015,15(3):19-24.
CHEN Weihua,YAN Xiaoyuan,FU Hua. Application of improved Elman neural network in prediction of gas emission ［J］. Journal of Safety and Environment,2015,15 (3):19-24.
［6］付华,王福娇,陈子春. 基于分数阶神经网络的瓦斯涌出量预测［J］. 传感器与微系统,2013,32(5):31-34.
FU Hua,WANG Fujiao,CHEN Zichun. Based on the fractional order neural network to forecast the gas emission of the ［J］. Journal of Sensors and Micro Systems,2013,32 (5):31-34.
［7］吕伏,梁冰,孙维吉,等. 基于主成分回归分析法的回采工作面瓦斯涌出量预测［J］.煤炭学报,2012,37(1):113-116.
LYU Fu,LIANG Bing,SUN Weiji,et al. Based on the principal component regression analysis of the working face gas emission prediction ［J］. Journal of Coal,2012,37(1):113-116.
［8］付华,姜伟,单欣欣. 基于耦合算法的煤矿瓦斯涌出量预测模型研究［J］. 煤炭学报,2012,37(4):654-658.
FU Hua,JIANG Wei,SHAN Xinxin. Coal mine gas emission prediction model based on coupling algorithm study ［J］. Journal of Coal,2012,37(4):654-658.
［9］付华,于翔,卢万杰.基于蚁群粒子群混合算法与LS-SVM瓦斯涌出量预测［J］. 传感技术学报，2016,29(3):373-377.
FU Hua,YU Xiang,LU Wanjie. Hybrid particle swarm based on ant colony algorithm and LS-SVM to forecast the gas emission ［J］. Journal of Sensing Technology,2016,29 (3):373-377.
［10］王福建,李铁强,俞传正. 道路交通事故灰色Verhulst预测模型［J］. 交通运输工程学报,2006,6(1):122-126.
WANG Fujian,LI Tieqiang,YU Chuanzheng. Grey Verhulst prediction model for road traffic accidents［J］. Journal of Transportation Engineering,2006,6(1):122-126.
［11］杜江,袁中华,王景芹. 一种基于灰预测理论的混合蛙跳算法［J］.电工技术学报,2017,32(15):190-198.
DU Jiang,YUAN Zhonghua,WANG Jingqin. One kind based on the ash forecast theory the mix frog jumps the algorithm［J］. Transactions of China Electrotechnical Society,2017,32( 15):190-198.
［12］宋晓华,杨尚东,刘达. 基于蛙跳算法的改进支持向量 机预测方法及应用［J］.中南大学学报(自然科学版),2011,42(9):2737-2740.
SONG XiaoHua,YANG Shangdong,LIU Da. Based on the leapfrog algorithm to improve the support vector machine prediction methods and the application of ［J］. Journal of Central South University(Natural Science Edition),2011,42(9):2737-2740.
［13］王文才,李刚,张世明.基于灰色理论的矿井瓦斯涌出量预测研究［J］.煤矿开采,2011,16(3):53-58.
WANG Wencai,LI Gang,ZHANG Shiming. Mine gas emission prediction research based on gray theory ［J］. Journal of Coal Mining,2011,16(3):53-58.
［14］陈洋,刘恩,陈大力.瓦斯涌出量分源预测法的发展与实践研究［J］.煤矿安全,2010,41(2):73-76.
CHEN Yang,LIU En,CHEN Dali. Research on the development and practice of the method of predicting gas emission by source of sources ［J］. Coal Mine Safety,2010,41(2):73-76.
［15］李杰,康天合,康官先.基于 IGSA-ELM 模型的回采工作面瓦斯涌出量预测［J］.煤矿安全,2016,47(1):155-158.
LI Jie,KANG Tianhe,KANG Guanxian. Returns based on the IGSA-ELM model picks the working surface gas to gush out the quantity to forecast ［J］. Coal Mine Safety,2016,47(1):155-158.
［16］张燕朋. 基于GM(1,1)模型的矿井瓦斯涌出量预测研究［J］. 煤炭技术,2012,31(2):101-103.
ZHANG Yanpeng. Gush out the quantity based on the GM(1,1)model damp to forecast studies ［J］. Coal Technology,2012,31(2):101-103.
［17］雷文杰,刘瑞涛.灰色关联优化BP神经网络预测工作面瓦斯涌出量［J］.矿业安全与环保,2013(5):33-41.
LEI Wenjie,LIU Ruitao. Grey correlation to optimize the BP neural network to predict coal face gas emission ［J］. Journal of Mining Safety and Environmental Protection,2013(5):33-41.
［18］马建宏,陈懿博,庞泽明.综放工作面瓦斯涌出量预测方法及工程实践［J］.中国安全生产科学技术,2014,10(10):143-147.
MA Jianhong,CHEN Yibo,PANG Zeming. Prediction method and engineering practice of gas emission in fully-mechanized caving face ［J］. Journal of Safety Science and Technology,2014,10 (10):143-147.
［19］李润求,吴莹莹,施式亮,等.煤矿瓦斯涌出时序预测的自组织数据挖掘方法［J］.中国安全生产科学技术,2017,13(7):18-23.
LI Runqiu,WU Yingying,SHI Shiliang,et al. Coal mine gas emission self-organizing data mining method for prediction of time series ［J］. Journal of Safety Science and Technology,2017,13 (7):18-23.
相似文献/References:
[1]彭程,潘玉民.粒子群优化的RBF瓦斯涌出量预测[J].中国安全生产科学技术,2011,7(11):77.
PENG Cheng,PAN Yu-min.Particle swarm optimization RBF for gas emission prediction[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2011,7(3):77.
[2]罗景峰,许开立.基于可变模糊组合方法的瓦斯涌出量预测[J].中国安全生产科学技术,2011,7(6):29.
LUO Jing-feng,XU Kai-li.Gas Emission Rate Forecast Based on variable fuzzy Combination method [J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2011,7(3):29.
[3]马建宏,陈懿博,庞泽明.综放工作面瓦斯涌出量预测方法及工程实践[J].中国安全生产科学技术,2014,10(10):143.[doi:10.11731/j.issn.1673-193x.2014.10.024]
MA Jian-hong,CHEN Yi-bo,PANG Ze-ming.Prediction method and engineering practice of gas emission in fully mechanized top coal caving face[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(3):143.[doi:10.11731/j.issn.1673-193x.2014.10.024]
[4]程成,胡杰,龚选平,等.采空区瓦斯涌出的回采速度效应分析[J].中国安全生产科学技术,2019,15(12):78.[doi:10.11731/j.issn.1673-193x.2019.12.013]
CHENG Cheng,HU Jie,GONG Xuanping,et al.Analysis on effect of mining speed on gas emission of goaf[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2019,15(3):78.[doi:10.11731/j.issn.1673-193x.2019.12.013]
[5]马恒,任美学,高科.基于随机搜索优化XGBoost的瓦斯涌出量预测模型*[J].中国安全生产科学技术,2022,18(5):129.[doi:10.11731/j.issn.1673-193x.2022.05.020]
MA Heng,REN Meixue,GAO Ke.Prediction model of gas emission amount based on XGBoost optimized with random search algorithm[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(3):129.[doi:10.11731/j.issn.1673-193x.2022.05.020]
备注/Memo
- 备注/Memo:
- 国家自然科学基金项目（51574143）
