基于云计算数据集成模式的矿井瓦斯预警研究
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摘要
矿井瓦斯一直是我国煤矿主要的灾害形式之一，并严重困扰着煤矿的安全生产。研究矿井日常检/监测数据的有效处理及其预测预警应用，有利于拓展安全监测监控系统的功能，是提高瓦斯灾害预警能力的重要手段。本论文在分析矿井瓦斯检/监测数据特征及其集成管控模式的基础上，深入研究了基于云计算数据集成模式下的矿井瓦斯预警分析理论和方法。
研究了矿井瓦斯检/监测数据的特点及其集成管控模式。分析了瓦斯检/监测数据的特征，并对于环境、人为、管理等因素影响下存在的异常数据、数据缺失问题，针对其特征进行平滑处理，使其形成完整的监测数据序列，符合监测数据整体统计特性，并构建了云计算模式下检/监测数据集成管控模式。
研究了矿井瓦斯浓度变化趋势预测预警方法。在瓦斯监测数据预处理的基础上，基于时间序列分析的自回归滑动平均（ARMA）模型，建立了适用于实时监测数据的瓦斯浓度动态趋势预测预警分析模型，结合实时预测结果与所在时段瓦斯监测数据的统计特征实现了瓦斯浓度变化趋势的动态预警。
研究了矿井瓦斯突出危险性预测预警方法。通过分析瓦斯实时监测数据的特征，提取反映瓦斯浓度实时变化趋势的参数、瓦斯浓度变化速率的参数以及用于表达瓦斯涌出特征的参数，结合防突检测参数，基于v-支持向量机（v-SVM）模型，构建了瓦斯突出危险性预测预警模型，结合瓦斯突出危险性预测结果与防突检测数据的统计特征，实现了瓦斯突出危险性预警。
研究了矿井瓦斯预警的云计算模型架构。基于云计算的原理，构建了应用于矿井瓦斯预警分析的云计算模式的物理架构及其云计算平台模式，并将矿井瓦斯检/监测数据处理及预测预警算法予以封装，为瓦斯预警计算的程序化服务构建了云计算模式，实现了高效的预警分析。
研究了云计算数据集成模式下瓦斯预警分析应用。基于所建立的瓦斯预警数学模型，将瓦斯检/监测数据处理的云计算模式应用于现场预警分析，经过实际检/监测数据的对照检验，表现出了良好的适用性和有效性。
本论文研究的云计算数据集成模式下瓦斯预警分析理论和方法，适用于煤矿现场的瓦斯预警分析应用，为煤矿瓦斯灾害防治提供了新的数字化平台构建方法和手段。
As one of the major hazards, mine gas seriously impedes safe production in coal mines.The study on effective analysis of mine monitoring data for the pre-warning of abnormal gassituation is important means to expand functions of the safety monitoring and control systemas well as to improve safety pre-warning. Based on the monitoring data and data integration,mine gas pre-warning under cloud computing environment is studied.
Characteristics of gas monitoring data and its integration models are studied. The timesequence of gas monitoring data is re-built in a smooth process on the basis of statistics so asto treat data defects caused by sensor errors and irregularity of measurement due to variousinterferences from environment, human and management factors, thus the data is ready forcloud computing.
Mathematical models of gas concentration prediction and pre-warning of abnormal gasemission is studied. By performing data pre-processing and the ARMA time sequenceanalysis, a dynamic prediction and pre-warning model for the monitoring system is putforward, and the on-line dynamic pre-warning is realized based on real-time prediction of gasconcentration and statistics of gas measurements.
Hazard prediction and pre-warning of mine gas outburst is studied. By identifying thecharacteristics of monitoring data, the progressive trend and the drift rate of gas concentration,features of gas emission, and the risks of gas outburst can be judged, and gas outburst hazardprediction and pre-warning models based on v-SVM platform is constructed, and on-site riskprediction of gas outburst is realized.
A model of mine gas pre-warning supported by cloud computing is studied, and thephysical structure and platform model of cloud computing suitable for gas pre-warninganalysis is put forward. Then the algorithms of data processing and gas pre-warning areencapsulated, and services of cloud computing as well as effective pre-warning analysis arerealized.
The gas pre-warning under cloud computing environment is studied. Based on gaspre-warming models, the pre-warning analysis supported by cloud computing is adopted andpromoted for on-site application. Verified by systematically obtained monitoring data, itshows that the models exhibit good applicability and validity.
Therefore, this study of gas pre-warning analysis theory and methods under cloudcomputing environment is applicable to mine site. It is capable to offer scientific thinking forthe digital platforms of mine gas disaster prevention and control.
研究了矿井瓦斯检/监测数据的特点及其集成管控模式。分析了瓦斯检/监测数据的特征，并对于环境、人为、管理等因素影响下存在的异常数据、数据缺失问题，针对其特征进行平滑处理，使其形成完整的监测数据序列，符合监测数据整体统计特性，并构建了云计算模式下检/监测数据集成管控模式。
研究了矿井瓦斯浓度变化趋势预测预警方法。在瓦斯监测数据预处理的基础上，基于时间序列分析的自回归滑动平均（ARMA）模型，建立了适用于实时监测数据的瓦斯浓度动态趋势预测预警分析模型，结合实时预测结果与所在时段瓦斯监测数据的统计特征实现了瓦斯浓度变化趋势的动态预警。
研究了矿井瓦斯突出危险性预测预警方法。通过分析瓦斯实时监测数据的特征，提取反映瓦斯浓度实时变化趋势的参数、瓦斯浓度变化速率的参数以及用于表达瓦斯涌出特征的参数，结合防突检测参数，基于v-支持向量机（v-SVM）模型，构建了瓦斯突出危险性预测预警模型，结合瓦斯突出危险性预测结果与防突检测数据的统计特征，实现了瓦斯突出危险性预警。
研究了矿井瓦斯预警的云计算模型架构。基于云计算的原理，构建了应用于矿井瓦斯预警分析的云计算模式的物理架构及其云计算平台模式，并将矿井瓦斯检/监测数据处理及预测预警算法予以封装，为瓦斯预警计算的程序化服务构建了云计算模式，实现了高效的预警分析。
研究了云计算数据集成模式下瓦斯预警分析应用。基于所建立的瓦斯预警数学模型，将瓦斯检/监测数据处理的云计算模式应用于现场预警分析，经过实际检/监测数据的对照检验，表现出了良好的适用性和有效性。
本论文研究的云计算数据集成模式下瓦斯预警分析理论和方法，适用于煤矿现场的瓦斯预警分析应用，为煤矿瓦斯灾害防治提供了新的数字化平台构建方法和手段。
As one of the major hazards, mine gas seriously impedes safe production in coal mines.The study on effective analysis of mine monitoring data for the pre-warning of abnormal gassituation is important means to expand functions of the safety monitoring and control systemas well as to improve safety pre-warning. Based on the monitoring data and data integration,mine gas pre-warning under cloud computing environment is studied.
Characteristics of gas monitoring data and its integration models are studied. The timesequence of gas monitoring data is re-built in a smooth process on the basis of statistics so asto treat data defects caused by sensor errors and irregularity of measurement due to variousinterferences from environment, human and management factors, thus the data is ready forcloud computing.
Mathematical models of gas concentration prediction and pre-warning of abnormal gasemission is studied. By performing data pre-processing and the ARMA time sequenceanalysis, a dynamic prediction and pre-warning model for the monitoring system is putforward, and the on-line dynamic pre-warning is realized based on real-time prediction of gasconcentration and statistics of gas measurements.
Hazard prediction and pre-warning of mine gas outburst is studied. By identifying thecharacteristics of monitoring data, the progressive trend and the drift rate of gas concentration,features of gas emission, and the risks of gas outburst can be judged, and gas outburst hazardprediction and pre-warning models based on v-SVM platform is constructed, and on-site riskprediction of gas outburst is realized.
A model of mine gas pre-warning supported by cloud computing is studied, and thephysical structure and platform model of cloud computing suitable for gas pre-warninganalysis is put forward. Then the algorithms of data processing and gas pre-warning areencapsulated, and services of cloud computing as well as effective pre-warning analysis arerealized.
The gas pre-warning under cloud computing environment is studied. Based on gaspre-warming models, the pre-warning analysis supported by cloud computing is adopted andpromoted for on-site application. Verified by systematically obtained monitoring data, itshows that the models exhibit good applicability and validity.
Therefore, this study of gas pre-warning analysis theory and methods under cloudcomputing environment is applicable to mine site. It is capable to offer scientific thinking forthe digital platforms of mine gas disaster prevention and control.
引文
[1]刘星魁,谢金亮.煤矿安全生产现状及对策探讨[J].煤炭技术,2008,27(1):139-140.
[2]黄盛初,刘文革,徐亮.中国煤炭行业海外发展战略研究[J].中国煤炭,2010,36(7):11-14.
[3]国家煤炭工业网[EB/OL]. http://www.coalchina.org.cn,2014-4-21.
[4]国家煤矿安全监察局[EB/OL]. www.chinacoal-safety.gov.cn,2013-12-10.
[5]卢鉴章,刘见中.煤矿灾害防治技术现状与发展[J].煤炭科学技术,2006,(5):1-5.
[6]李树刚,林海飞,成连华,等.我国煤矿瓦斯灾害事故发生原因及防治对策[A].中国职业安全健康协会2010年学术年会击丈集[C].2010.
[7]马丕梁,范启炜.我国煤矿抽放瓦斯现状及展望[J].中国煤炭,2004,30(2):5-7.
[8]王建政.构建本质安全化煤矿刍议[J].中国矿业大学学报（社会科学版）,2007,(2):76-78.
[9]汪云甲,杨敏,张克.数字矿山与煤矿瓦斯监测及预警[J].地理信息世界,2008,(5):26-31.
[10]国家煤矿安全监察局.国家安全监管总局国家煤矿安监局关于近期几起煤矿事故的通报[J].国家安全生产监督管理总局国家煤矿安全监察局公告,2010(1):22-25.
[11]彭立军.矿井监测监控的发展[J].科技情报开发与经济,2003,(4):205-206.
[12]周邦全.煤矿安全监测监控系统的发展历程和趋势[J].矿业安全与环保,2007,34：76-77.
[13]刘源骏,袁梅,马科伟.煤矿安全监测监控系统探讨[J].矿业工程,2010,8(2):49-51
[14]颜爱华.煤层瓦斯含量多源数据分析及其预测研究[D].北京:中国矿业大学,2009.
[15] Tang Hong-wei, Cheng Jian-yuan, Wang Shi-dong. The effective application of BPneural networks prediction model for gas content in binchang mining[A]. Proceedingsof the2009International Conference on Computational Intelligence and SoftwareEngineering [C].2009.
[16] Bo Zhai, Jianfeng Shan. Gas Content Prediction Based on GA-RBF Neural Network[A].2010Chinese Control and Decision Conference[C].2010.
[17] Wu Shi-yue, Guo Yong-yi, Li Yuan-xing, et al. Research on the mechanism of coal andgas outburst and the screening of prediction indices[A]. The6th InternationalConference on Mining Science&Technology[C].2009.
[18] WU Dongmei, ZHAO Yuemin, CHENG Yuanping, et al. P index with different gascompositions for instantaneous outburst prediction in coal mines [J]. Mining Scienceand Technology,2010,20:723-726.
[19] Peer, M. Evaluation of a mathematical model using experimental data and artificialneural network for prediction of gas separation [J]. Journal of Natural Gas Chemistry,2008,17(2):135-141.
[20] Fu Xue-hai, Zhang Wen-ping, Zhou Ya-nan, et al. Technology and method of coal andgas outburst prediction during coal geological exploration[A].The6th InternationalConference on Mining Science&Technology[C].2009.
[21]于洪仕,付兴.煤与瓦斯突出声发射的神经网络预测方法[J].黑龙江科技学院学报,2006,16(2):71-73.
[22]王亚军,金芳勇,李江涛.煤与瓦斯突出的混沌—分形特征分析[J].煤矿安全,2010,(10):88-90.
[23] Yang Sheng-qiang, Sun Yan, Chen Zu-yun. Establishment of grey-neural networkforecasting model of coal and gas outburst[J]. Procedia Earth and Planetary Science,2009,(1):148-153.
[24]张瑞林.现代信息技术在煤与瓦斯突出区域预测中的应用[D].重庆:重庆大学,2004.
[25]撒占友,何学秋,王恩元.煤与瓦斯突出危险性电磁辐射异常判识方法[J].煤炭学报,2008,33(12):1374-1375.
[26]赵志刚.煤与瓦斯突出的藕合灾变机制及非线性分析[D].青岛:山东科技大学,2007.
[27]王汉斌.煤与瓦斯突出的分形预测理论及应用[D].太原:太原理工大学,2009.
[28] LI Sheng, ZHANG Hong-wei. Pattern Recognition and Forecast of Coal and GasOutburst [J]. J. China Univ. of Mining&Tech.(English Edition),2005,15(3):251-254.
[29] LI Sheng, WANG Qi-jiang, LUAN Qiao-lin. Development of regional predictioninformation system of coal and gas outburst [J]. Journal of Coal Science&Engineering(China),2009,(3).
[30] WANG Zhi-rong, CHEN Ling-xia, CHENG Cong-ren, et al. Forecast of Geological GasHazards for Three-Soft Coal Seams in Gliding Structural Areas [J]. Journal of ChinaUniversity of Mining&Technology,2007,17(4):484-488.
[31] PENG Shou-jian, XU Jiang TAO, Yun-qi, et al. Matter-elements model and applicationfor prediction of ccoal and gas outburst[J]. Journal of Coal Science&Engineering(China),2009,(3).
[32] JIANG Cheng-lin, LI Zeng-hua, HAN Ying. Distribution of Heavy Hydrocarbon inCoal Seams and Its Use in Predicting Outburst of Coal [J]. Journal of China Universityof Mining&Technology,2003,13(1):29-32.
[33] GUO De-yong, SONG Guang-tai, KU Ming-xin. Research on coal structure indices tocoal and gas outbursts in Pingdingshan Mine Area, China [J]. Journal of Coal Science&Engineering (China),2002,(1).
[34] Xiao-Zhao Li, An-Zeng Hua. Prediction and prevention of sandstone-gas outbursts incoal mines [J]. International Journal of Rock Mechanics and Mining Sciences,2006,43(1):2-18.
[35] Zhang Xiaoming, Sun Bingyu, Wang Rujing, et al. Application of GA based rough settheory in forecast of gas outbursts[A].2008International Symposium on ComputationalIntelligence and Design[C].2008.
[36]金芳勇.基于分形—混沌理论的煤与瓦斯突出预测研究[D].合肥:安徽理工大学,2006.
[37]陈祖云.煤与瓦斯突出前兆的非线性预测及支持向量机识别研究[D].徐州:中国矿业大学,2009.
[38]肖红飞,彭斌.基于自记忆模型的煤与瓦斯突出电磁辐射预测研究[J].中国安全科学学报,2009,19(10):88-91.
[39]邹云龙,赵旭生,孙东玲.利用炮掘工作面瓦斯涌出时间序列预测工作面突出危险性[A].中国煤炭学会煤矿安全专业委员会2009年学术研讨会交流论文[C].2009.
[40]邓明,张国枢,陈清华.基于瓦斯涌出时间序列的煤与瓦斯突出预报[J].煤炭学报,2010,35(2):206-263.
[41] Chao WANG, Enyuan WANG, Jiankun XU, et al. Bayesian discriminant analysis forprediction of coal and gas outbursts and application[J]. Mining Science and Technology(China),2010,20(4):520-523.
[42] CAO Qing-kui, LI Li-jie, YU Bing. Application of dynamic set-pair analysis in coal andgas outburst prediction[J]. Journal of Coal Science&Engineering (China),2008,(1).
[43]张豫生.基于地质构造的煤与瓦斯突出预测研究[D].阜新:辽宁工程技术大学,2006.
[44] ZHANG Hong-wei, SONG Wei-hua, YANG Heng, et al. Pattern recognition predictionof coal and gas outburst hazard in the sixth mine of Hebi[J]. Journal of Coal Science&Engineering (China),2008,(2).
[45] Song Wei-hua, Zhang Hong-wei. Regional prediction of coal and gas outburst hazardbased on multi-factor pattern recognition[J]. Procedia Earth and Planetary Science,2009,1(1):347-353.
[46] Yang Min, Yun-jia Wang, Yuan-ping Cheng. An incorporate genetic algorithm basedback propagation neural network model for coal and gas outburst intensity prediction[J].Procedia Earth and Planetary Science,2009,1(1):1285-1292.
[47]李刚.瓦斯浓度的分形分析与混沌预测模型研究[D].北京:中国矿业大学,2004.
[48] Qiao Meiying, Ma Xiaoping, Lan Jianyi, et al. Time-series gas prediction model usingLS-SVR within a Bayesian framework[J]. Mining Science and Technology (China),2011,21:153-157.
[49] Hua Fu, Dan Zhao, Xiao Lv. Optimized LS_SVM predictive control of gas outburstbased on improved PSO[A].2009International Conference on Information Engineeringand Computer Science[C].2009.
[50]李树刚,刘志云.综采面矿山压力与瓦斯涌出监测研究[J].矿山压力与顶板管理,2002,(1):99-101.
[51]戴广龙,汪有清,张纯.保护层开采工作面瓦斯涌出量预测[J].煤炭学报,2007,32(4):382-385.
[52]谢生荣,赵耀江.综采工作面的瓦斯涌出规律及涌出量的预测[J].太原理工大学学报.2005,36(5):553-556.
[53]杨东.采动落煤瓦斯涌出量预测模型的建立及应用[J].煤矿安全,2008,26(10):15-17.
[54]徐德金,胡宝林.基于GIS技术的瓦斯涌出量预测[J].煤矿安全,2009,35(5):94-96.
[55]都锋,刘恩,仇海生,等.回归分析法在预测瓦斯涌出量中的应用[J].煤矿安全,2010,(3):26-27.
[56]郭德勇,郑茂杰,鞠传磊,等.采煤工作面瓦斯涌出量预测逐步回归方法[J].北京科技大学学报,2009,31(9):1095-1097.
[57]蔡武,袁莎莎,戚洋.基于多元线性回归分析法预测矿井瓦斯涌出量[J].煤矿安全,2010,(1):91-94.
[58]伍爱友,田云丽,宋译,等.灰色系统理论在矿井瓦斯涌出量预测中的应用[J].煤炭学报,2005,30(5):389-392.
[59]陶云奇,许江,李树春.改进的灰色马尔柯夫模型预测采煤工作面瓦斯涌出量[J].煤炭学报,2007,32(4):391-395.
[60]詹海光,尹利平.采煤工作面瓦斯涌出量的聚类预测[J].矿业工程研究,2009,24(3):38-40.
[61]何华刚,胡敏涛.基于改进CC方法的瓦斯涌出混沌预测[J].矿业研究与开发,2009,29(4):69-71.
[62]赵红梅,陈开岩.R/S分形理论在瓦斯涌出量预测中的应用研究[J].山东煤炭科技,2010,(3):172-175.
[63]何俊,潘结南,聂百胜.瓦斯涌出严重程度分形预测研究[J].中国安全科学学报,2006,16(5):22-26.
[64] C. zgen, Karacan. Modeling and prediction of ventilation methane emissions ofU.S.longwall mines using supervised artificial neural networks[J]. International Journalof Coal Geology,2008,73:371-387.
[65]朱红青,常文杰,张彬.回采工作面瓦斯涌出BP神经网络分源预测模型及应用[J].煤炭学报,2007,32(5):504-507.
[66]施式亮,宋译,何利文.矿井掘进工作面瓦斯涌出混沌特性判别研究[J].煤炭学报,2006,31(6):702-705.
[67]黄为勇,童敏明,任子晖.基于SVM的瓦斯涌出量非线性组合预测方法[J].中国矿业大学学报,2009,38(2):235-238.
[68]马文涛.基于WT与GALSSVM的瓦斯涌出量预测[J].采矿与安全工程学报,2009,26(4):524-527.
[69]潘结南,陈江峰,徐文鹏. R/S分析及矿井瓦斯涌出量的分形预测[J].煤田地质与勘探,2001,(3):14-15.
[70] PAN Jie-nan, MENG Zhao-ping, LIU Ya-chuan. Grey Smoothing Model for PredictingMine Gas Emission[J]. Journal of China University of Mining&Technology,2003,13(1):76-78.
[71]王汉斌,郝仲熙,牛冲槐.瓦斯涌出规律非线性系统模型理论与应用研究[J].金属矿山,2006,(4):59-61.
[72]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):1666-1668.
[73]张朝举,邱进伟.基于Fuzzy时间序列模型矿井瓦斯涌出量预测研究[J].能源技术与管理,2006,(2):21-22.
[74]曾勇,吴财芳.矿井瓦斯涌出量预测的模糊分形神经网络研究[J].2004,34(2):62-64.
[75] XU Chang-kai, WANG Yao-cai, WANG Jun-wei. Research on Forecast Technology ofMine Gas Emission Based on Fuzzy Data Mining[J]. J. China Univ. of Mining&Tech.(English Edition),2004,14(2):174-178.
[76]孟海东,孙搏,司子稳,等.基于时间序列的矿井瓦斯涌出量预测方法[J].工矿自动化,2010,(12):24-26.
[77]徐刚,罗飞飞,邓绪彪.基于随机动态模型的矿井瓦斯涌出量预测研究[J].煤炭工程,2009,(8):79-82.
[78]何利文.煤矿回采工作面瓦斯涌出非线性特性分析及预测仿真理论研究[D].长沙:中南大学,2009.
[79]黄炜伟,王强,卢爽,等.基于混沌时间序列的神经网络对瓦斯涌出量预测[J].煤,2008,14(5):7-9.
[80] Jing Hong, Yi Zhao. The study of the gas emission prediction model based onfuzzy-rough set neural network[A]. Proceedings of the2nd International Conference onMachine Learning and Computing[C].2010.
[81] Yumin Pan, Xiaoyu Zhang, Pengqian Xue. The amount prediction of gas emitted viawavelet neural network with grey correlation analysis[A]. Proceedings2010SixthInternational Conference on Natural Computation[C].2010.
[82] Xiang Chen, Weihua Cai. Wavelet neural net based work face gas emissionprediction[A].2009IEEE International Conference on Intelligent Computing andIntelligent Systems[C].2009.
[83] Ning Yuncai, Chen Xiang. Coal face gas emission prediction based on support vectormachine[A]. Proceedings of the2009International Conference on Artificial Intelligenceand Computational Intelligence[C].2009.
[84] Wang Jianping, Huang Hongxia, Li Hongbiao, et al. Prediction of mine gas emissionrate based on the SVM model[A].2008Chinese Control Conference[C].2008.
[85] Wang Changlong, Fu Weihua. Gas emission rate prediction in fully-mechanizedexcavated faces based on support vector machine[A]. Proceedings of the20093rdInternational Symposium on Intelligent Information Technology Application Workshops
[C].2009.
[86]邵良杉.基于粗糙集理论的煤矿瓦斯预测技术[J].煤炭学报,2009,34(3):371-374.
[87] SHAO Liang-shan, FU Gui-xiang. Disaster prediction of coal mine gas based on datamining [J]. Journal of Coal Science&Engineering (China),2008,(3).
[88]吕品,马云歌,周心权.上隅角瓦斯浓度动态预测模型的研究及应用[J].煤炭学报,2006,31(4):462-465.
[89]段霄鹏.基于商空间的煤矿瓦斯数据挖掘研究[D].太原:太原理工大学,2010.
[90]崔小彦,赵国强.混沌预测在井下瓦斯浓度监测的应用[J].辽宁工程技术大学学报（自然科学版）,2008,27(5):185-186.
[91]施式亮,何利文,宋译,等.基于混沌与神经网络辐合模型的回采工作面瓦斯涌出时序分析与预侧[A].中国职业安全健康协会2008年学术年会论文集[C].2008.
[92]张剑英,许徽,陈娟,等.基于粒子群优化的支持向量机在瓦斯浓度预测中的应用研究[J].工矿自动化,2010,(10):32-35.
[93]王其军,程久龙.基于免疫神经网络模型的瓦斯浓度智能预测[J].煤炭学报,2008,33(6):666-668.
[94]白峻尧.基于SVM的瓦斯涌出的自学习模型研究应用[D].西安:西安科技大学,2006.
[95]李苏旺.时间序列数据建模及其在瓦斯预测中的应用研究[D].太原:太原理工大学,2003.
[96]杨胜远,李金林,杨东朋. ARMA模型在瓦斯浓度预测中的应用[J].中国煤炭,2008,34(6):14-16.
[97]程健,白静宜,钱建生,等.基于混沌时间序列的煤矿瓦斯浓度短期预测[J].中国矿业大学学报,2008,37(2):231-234.
[98] Jian Cheng, Jian-sheng Qian, Guang-dong Niu, Yi-nan Guo. Gas concentrationforecasting based on support vector regression in correlation space via KPCA [A].Advances in Neural Networks.6th International Symposium on Neural Networks
[C].2009.
[99]杜晓坤.改进的混沌预测方法在瓦斯监测中的应用[J].微计算机信息,2010,26(11):190-191.
[100]朱宇.构造神经网络在瓦斯时间序列预测中的应用[D].太原:太原理工大学,2010.
[101]高莉,胡延军,于洪珍.基于W-RBF的瓦斯时间序列预测方法[D].煤炭学报,2008,33(1):68-70.
[102] GAO Li, YU Hong-zhen. Prediction of Gas Emission Based on Information Fusion andChaotic Time Series [J]. J. China Univ. of Mining&Tech.(English Edition),2006,16(1):94-96.
[103] Ma Xian-Min. Coal gas outburst predication and control based on RBF neural network
[A].20103rd IEEE International Conference on Computer Science and InformationTechnology[C].2010.
[104]赵金宪,于光华.瓦斯浓度预测的混沌时序RBF神经网络模型[J].黑龙江科技学院学报,2010,2(2):131-134.
[105] Jin-Xian Zhao, Hong-Zhang Jin, Guang-Hua Yu. Coalmine gas concentrationforecasting based on chaotic theory and neural network model [A]. Proceedings of theThird International Conference on Information and Computing Science[C].2010.
[106] Qu Zhiming, Liang Xiaoying. Application of grey relation clustering and CGNN in gasconcentration prediction in top corner of coal mine[A].2009International Conferenceon Computational Intelligence and Natural Computing[C].2009.
[107] Zhang Yueqin, Duan Xiaopeng. Based on Cluster Cover Algorithm in Coal Mine GasForecast Application[A]. Proceedings of the2010International Conference onIntelligent Computation Technology and Automation[C].2010.
[108] Yujun Liu, Yueqin Zhang, Yu Zhu. A constructive neural network learning method basedon quotient space and its application in coal mine gas prediction[A].2010InternationalConference on Intelligent Computing and Cognitive Informatics[C].2010.
[109] Yuxiang Shao, Dongmei Zhang. Mine forecast based on genetic annealing neuralnetwork[A]. Proceedings2009International Conference on Information Technology andComputer Science[C].2009.
[110]刘婷婷,史久根,韩江洪.基于SVM的瓦斯体积分数混沌时间序列预测[J].合肥工业大学学报（自然科学版）,2009,32(8):1150-1153.
[111] Xiao-hu Zhao, Ke-ke Zhao. Study of an improved online least squares support vectormachine algorithm and its application in gas prediction[A].2008Fourth InternationalConference on Natural Computation[C].2008.
[112] Xiao-hu ZHAO, Gang WANG, Ke-ke ZHAO, et al. On-line least squares support vectormachine algorithm in gas prediction [J]. Mining Science and Technology,2009,19(2):194-198.
[113]王泉夫,赵小虎,王刚.在线最小二乘支持向量机在瓦斯预测中的研究与应用[A].2010（沈阳）国际安全科学与技术学术研讨会论文集[C].2010.
[114] Jian-sheng Qian, Jian Cheng, Yi-nan Guo. A novel multiple support vector machinesarchitecture for chaotic time series prediction[A]. Advances in Natural Computation.Second International Conference[C].2006.
[115]许徽,张剑英,陈娟,等.基于分段线性方法的瓦斯浓度时间序列模式表示[J].工矿自动化,2010,(8):41-44.
[116]贾真.神经网络在煤矿瓦斯预警系统中的应用研究[D].长春:长春工业大学,2007.
[117] Xiaosheng Liu, Xiaobin Huang. Research on pre-warning expert system of coal minegas safety based on object-oriented [A].2008IEEE International Symposium onKnowledge Acquisition and Modeling Workshop[C].2008.
[118]熊廷伟.煤矿瓦斯爆炸预警技术研究[D].重庆:重庆大学,2005.
[119]牛聚粉.基于Mapx的煤与瓦斯预测预警技术研究[D].北京:中国地质大学,2006.
[120]李华.矿井掘进瓦斯爆炸实时智能预警监控系统[D].西安:西安科技大学,2005.
[121] Jian-sheng QIAN, Hong-sheng YIN, Xiu-rong LIU, et al. Data Processing Model ofCoalmine Gas Early-Warning System. Journal of China University of Mining andTechnology,2007,17(10):20-24.
[122] Xian-Min Ma. Application of data fusion theory in coal gas fire prediction system[A].2008International Conference on Intelligent Computation Technology and Automation[C].2008.
[123] Jipeng Lin. Pre-alarm System Of Coal Mine Based On ICA and Kalman Filter[A].Proceedings2010Sixth International Conference on Natural Computation[C].2010.
[124] Xie Guo-min, Fu Hua, Wang Shi-yun. Study On Early Warning Of Gas In Coal MineBased On Extension Data Mining [A].20102nd International Conference on EducationTechnology and Computer[C].2010.
[125] Jin-xing Song, Yu-fang Liu. Study on the mine gas disaster prevention system by GIS
[A].20102nd Conference on Environmental Science and Information ApplicationTechnology[C].2010.
[126]高莉,胡延军,于洪珍.基于W-RBF的瓦斯时间序列预测方法[D].煤炭学报,2008,33(1):68-70.
[127] GAO Li, YU Hong-zhen. Prediction of Gas Emission Based on Information Fusion andChaotic Time Series [J]. J. China Univ. of Mining&Tech.(English Edition),2006,16(1):94-96.
[128]施式亮,何利文,宋译,等.基于混沌与神经网络辐合模型的回采工作面瓦斯涌出时序分析与预侧[A].中国职业安全健康协会2008年学术年会论文集[C].2008.
[129]王其军,程久龙.基于免疫神经网络模型的瓦斯浓度智能预测[J].煤炭学报,2008,33(6):666-668.
[130] Yuxiang Shao, Dongmei Zhang. Mine forecast based on genetic annealing neuralnetwork[A]. Proceedings2009International Conference on Information Technology andComputer Science[C].2009.
[131]朱宇.构造神经网络在瓦斯时间序列预测中的应用[D].太原:太原理工大学,2010.
[132]白峻尧.基于SVM的瓦斯涌出的自学习模型研究应用[D].西安:西安科技大学,2006.
[133] Jian-sheng Qian, Jian Cheng, Yi-nan Guo. A novel multiple support vector machinesarchitecture for chaotic time series prediction[A]. Advances in Natural Computation.Second International Conference[C].2006.
[134] Jian Cheng, Jian-sheng Qian, Guang-dong Niu, et al. Gas concentration forecastingbased on support vector regression in correlation space via KPCA [A]. Advances inNeural Networks.6th International Symposium on Neural Networks [C].2009.
[135]王泉夫,赵小虎,王刚.在线最小二乘支持向量机在瓦斯预测中的研究与应用[A].2010（沈阳）国际安全科学与技术学术研讨会论文集[C].2010.
[136] Xiao-hu ZHAO, Gang WANG, Ke-ke ZHAO, et al. On-line least squares support vectormachine algorithm in gas prediction [J]. Mining Science and Technology,2009,19(2):194-198.
[137] Xiao-hu Zhao, Ke-ke Zhao. Study of an improved online least squares support vectormachine algorithm and its application in gas prediction[A].2008Fourth InternationalConference on Natural Computation[C].2008.
[138]崔小彦,赵国强.混沌预测在井下瓦斯浓度监测的应用[J].辽宁工程技术大学学报（自然科学版）,2008,27(5):185-186.
[139]钟震宇.小煤矿通风瓦斯集群监控系统的研究[D].武汉:中国地质大学,2006.
[140]杨禹华,钟震宇,蔡康旭.基于模糊模式识别的瓦斯含量指标异常预警技术[J].中国安全科学学报,2007,17(9):172-175.
[141] Shu-gang CAO, Yan-bao LIU, Yan-ping WANG. A forecasting and forewarning modelfor methane hazard in working face of coal mine based on LS-SVM[J]. Journal of ChinaUniversity of Mining and Technology,2008,18(2):172-176.
[142]黄为勇.基于支持向量机数据融合的矿井瓦斯预警技术研究[D].徐州:中国矿业大学,2009.
[143]邵良杉,付贵祥.基于数据融合理论的煤矿瓦斯动态预测技术[J].煤炭学报,2008,33(5):551-554.
[144]李骥平.商空间理论在煤矿预警中的应用[D].太原:太原理工大学,2010.
[145]张棋胜.云计算平台监控系统的研究与应用[D].北京:北京交通大学,2011.
[146]张玄.基于云计算的软件资源服务平台研究[D].上海:东华大学,2012.
[147]孙伟龙.基于IaaS云计算的Web应用技术研究[D].南京:南京理工大学,2011.
[148]刘枫.基于Google与计算平台的Web应用系统设计及实现[D].成都:电子科技大学,2011.
[149]王柳峰.基于虚拟化的云计算平台内存资源协同共享技术研究[D].长沙:国防科学技术大学,2011.
[150]刘永波,代君.云计算环境下的信息资源整合技术研究[J].计算机光盘软件与应用,2012,(8):24-25.
[151]刘秀.云计算中的数据管理关键技术及其应用[J].信息与电脑,2012,(4):121-122.
[152]冷鹏.基于云计算的信息管理系统的研究[J].制造业自动化,2012,34(6):7-9.
[153]李桂银.基于云计算的信息共享空间资源建设[J].图书馆现代化技术,2011,32(2):53-55.
[154]张杰.神华宁煤集团安全智能分析平台建设构想[J].工矿自动化,2012,(9):83-86.
[155]张杰.物联网技术在神华宁煤集团数字化矿山建设中的应用研究[J].煤矿现代化,2012,(5):60-63.
[156]程宽,臧东辉,张国锋.基于云计算的煤矿安全监管信息系统[J].应用能源技术,2012,(4):10-12.
[157]郝雪梅.基于云计算的煤炭企业信息化研究[D].太原:山西财经大学,2011.
[158]杨叔子,吴雅,轩建平,等.时间序列分析的工程应用（第二版）[M].武汉:华中科技大学出版社,2007.
[159]施仁杰,卢科学.时间序列分析引论[M].西安:西安电子科技大学出版社,1988.
[160]杨位钦,顾岚.时间序列分析与动态数据建模[M].北京:北京工业学院出版社,1989.
[161]王沁.时间序列分析及其应用[M].成都:西南交通大学出版社,2008.
[162] R.A.Davis. Introduction to time series and forecasting[M]. Beijing: Post&TelecomPress,2009.
[163]杨胜远,李金林,杨东朋. ARMA模型在瓦斯浓度预测中的应用[J].中国煤炭,2008,34(6):14~16.
[164]陶剑文,王士同.局部保留最大信息差v-支持向量机[J].自动化学报,2012,38(1):98-103.
[165]吴奇,严洪森.基于混沌v-支持矢量机的产品销售预测模型[J].机械工程学报,2010,46(7):128-136.
[166]范玉妹,郭春静.支持向量机算法的研究及其实现[J].河北工程大学学报（自然科学版）,2010,27(4):106-110.
[167]李向军,庄彬,孟志青.基于v-SVM的时态周期模式发现[J].微电子学与计算机,2009,26(11):169-176.
[168]刘华富,王仲.核函数对v-支持向量机的泛化能力影响分析[J].计算机工程与科学,2007,29(7):76-79.
[169] GAO Cheng-Feng, CHEN Tian-Lun, NAN Tian-Shi. Discussion of Some ProblemsAbout Nonlinear Time Series Prediction Using v-Support Vector Machine[J].Communications in Theoretical Physics,2010,(7):117-124.
[170] TAO JianWen, CHUNG FuLai, WANG ShiTong. A kernel learning framework fordomain adaptation learning [J]. Science China (Information Sciences),2012(9):1985-1920.
[171] WU Jing-min, ZUO Hong-fu, CHEN Yong. An estimation method for directmaintenance cost of aircraft components based on particle swarm optimization withimmunity algorithm [J]. Journal of Central South University of Technology (EnglishEdition),2005,21(2):95-101.
[172]王淑芳. v参数SVM在烟气轮机故障诊断中的应用[J].现代电子技术,2012,35(5):43-51.
[173]李益国,沈炯.基于v-支持向量机的边际电价预测及置信区间估计[J].东南大学学报（自然科学版）,2007,37(1):70-75.
[2]黄盛初,刘文革,徐亮.中国煤炭行业海外发展战略研究[J].中国煤炭,2010,36(7):11-14.
[3]国家煤炭工业网[EB/OL]. http://www.coalchina.org.cn,2014-4-21.
[4]国家煤矿安全监察局[EB/OL]. www.chinacoal-safety.gov.cn,2013-12-10.
[5]卢鉴章,刘见中.煤矿灾害防治技术现状与发展[J].煤炭科学技术,2006,(5):1-5.
[6]李树刚,林海飞,成连华,等.我国煤矿瓦斯灾害事故发生原因及防治对策[A].中国职业安全健康协会2010年学术年会击丈集[C].2010.
[7]马丕梁,范启炜.我国煤矿抽放瓦斯现状及展望[J].中国煤炭,2004,30(2):5-7.
[8]王建政.构建本质安全化煤矿刍议[J].中国矿业大学学报（社会科学版）,2007,(2):76-78.
[9]汪云甲,杨敏,张克.数字矿山与煤矿瓦斯监测及预警[J].地理信息世界,2008,(5):26-31.
[10]国家煤矿安全监察局.国家安全监管总局国家煤矿安监局关于近期几起煤矿事故的通报[J].国家安全生产监督管理总局国家煤矿安全监察局公告,2010(1):22-25.
[11]彭立军.矿井监测监控的发展[J].科技情报开发与经济,2003,(4):205-206.
[12]周邦全.煤矿安全监测监控系统的发展历程和趋势[J].矿业安全与环保,2007,34：76-77.
[13]刘源骏,袁梅,马科伟.煤矿安全监测监控系统探讨[J].矿业工程,2010,8(2):49-51
[14]颜爱华.煤层瓦斯含量多源数据分析及其预测研究[D].北京:中国矿业大学,2009.
[15] Tang Hong-wei, Cheng Jian-yuan, Wang Shi-dong. The effective application of BPneural networks prediction model for gas content in binchang mining[A]. Proceedingsof the2009International Conference on Computational Intelligence and SoftwareEngineering [C].2009.
[16] Bo Zhai, Jianfeng Shan. Gas Content Prediction Based on GA-RBF Neural Network[A].2010Chinese Control and Decision Conference[C].2010.
[17] Wu Shi-yue, Guo Yong-yi, Li Yuan-xing, et al. Research on the mechanism of coal andgas outburst and the screening of prediction indices[A]. The6th InternationalConference on Mining Science&Technology[C].2009.
[18] WU Dongmei, ZHAO Yuemin, CHENG Yuanping, et al. P index with different gascompositions for instantaneous outburst prediction in coal mines [J]. Mining Scienceand Technology,2010,20:723-726.
[19] Peer, M. Evaluation of a mathematical model using experimental data and artificialneural network for prediction of gas separation [J]. Journal of Natural Gas Chemistry,2008,17(2):135-141.
[20] Fu Xue-hai, Zhang Wen-ping, Zhou Ya-nan, et al. Technology and method of coal andgas outburst prediction during coal geological exploration[A].The6th InternationalConference on Mining Science&Technology[C].2009.
[21]于洪仕,付兴.煤与瓦斯突出声发射的神经网络预测方法[J].黑龙江科技学院学报,2006,16(2):71-73.
[22]王亚军,金芳勇,李江涛.煤与瓦斯突出的混沌—分形特征分析[J].煤矿安全,2010,(10):88-90.
[23] Yang Sheng-qiang, Sun Yan, Chen Zu-yun. Establishment of grey-neural networkforecasting model of coal and gas outburst[J]. Procedia Earth and Planetary Science,2009,(1):148-153.
[24]张瑞林.现代信息技术在煤与瓦斯突出区域预测中的应用[D].重庆:重庆大学,2004.
[25]撒占友,何学秋,王恩元.煤与瓦斯突出危险性电磁辐射异常判识方法[J].煤炭学报,2008,33(12):1374-1375.
[26]赵志刚.煤与瓦斯突出的藕合灾变机制及非线性分析[D].青岛:山东科技大学,2007.
[27]王汉斌.煤与瓦斯突出的分形预测理论及应用[D].太原:太原理工大学,2009.
[28] LI Sheng, ZHANG Hong-wei. Pattern Recognition and Forecast of Coal and GasOutburst [J]. J. China Univ. of Mining&Tech.(English Edition),2005,15(3):251-254.
[29] LI Sheng, WANG Qi-jiang, LUAN Qiao-lin. Development of regional predictioninformation system of coal and gas outburst [J]. Journal of Coal Science&Engineering(China),2009,(3).
[30] WANG Zhi-rong, CHEN Ling-xia, CHENG Cong-ren, et al. Forecast of Geological GasHazards for Three-Soft Coal Seams in Gliding Structural Areas [J]. Journal of ChinaUniversity of Mining&Technology,2007,17(4):484-488.
[31] PENG Shou-jian, XU Jiang TAO, Yun-qi, et al. Matter-elements model and applicationfor prediction of ccoal and gas outburst[J]. Journal of Coal Science&Engineering(China),2009,(3).
[32] JIANG Cheng-lin, LI Zeng-hua, HAN Ying. Distribution of Heavy Hydrocarbon inCoal Seams and Its Use in Predicting Outburst of Coal [J]. Journal of China Universityof Mining&Technology,2003,13(1):29-32.
[33] GUO De-yong, SONG Guang-tai, KU Ming-xin. Research on coal structure indices tocoal and gas outbursts in Pingdingshan Mine Area, China [J]. Journal of Coal Science&Engineering (China),2002,(1).
[34] Xiao-Zhao Li, An-Zeng Hua. Prediction and prevention of sandstone-gas outbursts incoal mines [J]. International Journal of Rock Mechanics and Mining Sciences,2006,43(1):2-18.
[35] Zhang Xiaoming, Sun Bingyu, Wang Rujing, et al. Application of GA based rough settheory in forecast of gas outbursts[A].2008International Symposium on ComputationalIntelligence and Design[C].2008.
[36]金芳勇.基于分形—混沌理论的煤与瓦斯突出预测研究[D].合肥:安徽理工大学,2006.
[37]陈祖云.煤与瓦斯突出前兆的非线性预测及支持向量机识别研究[D].徐州:中国矿业大学,2009.
[38]肖红飞,彭斌.基于自记忆模型的煤与瓦斯突出电磁辐射预测研究[J].中国安全科学学报,2009,19(10):88-91.
[39]邹云龙,赵旭生,孙东玲.利用炮掘工作面瓦斯涌出时间序列预测工作面突出危险性[A].中国煤炭学会煤矿安全专业委员会2009年学术研讨会交流论文[C].2009.
[40]邓明,张国枢,陈清华.基于瓦斯涌出时间序列的煤与瓦斯突出预报[J].煤炭学报,2010,35(2):206-263.
[41] Chao WANG, Enyuan WANG, Jiankun XU, et al. Bayesian discriminant analysis forprediction of coal and gas outbursts and application[J]. Mining Science and Technology(China),2010,20(4):520-523.
[42] CAO Qing-kui, LI Li-jie, YU Bing. Application of dynamic set-pair analysis in coal andgas outburst prediction[J]. Journal of Coal Science&Engineering (China),2008,(1).
[43]张豫生.基于地质构造的煤与瓦斯突出预测研究[D].阜新:辽宁工程技术大学,2006.
[44] ZHANG Hong-wei, SONG Wei-hua, YANG Heng, et al. Pattern recognition predictionof coal and gas outburst hazard in the sixth mine of Hebi[J]. Journal of Coal Science&Engineering (China),2008,(2).
[45] Song Wei-hua, Zhang Hong-wei. Regional prediction of coal and gas outburst hazardbased on multi-factor pattern recognition[J]. Procedia Earth and Planetary Science,2009,1(1):347-353.
[46] Yang Min, Yun-jia Wang, Yuan-ping Cheng. An incorporate genetic algorithm basedback propagation neural network model for coal and gas outburst intensity prediction[J].Procedia Earth and Planetary Science,2009,1(1):1285-1292.
[47]李刚.瓦斯浓度的分形分析与混沌预测模型研究[D].北京:中国矿业大学,2004.
[48] Qiao Meiying, Ma Xiaoping, Lan Jianyi, et al. Time-series gas prediction model usingLS-SVR within a Bayesian framework[J]. Mining Science and Technology (China),2011,21:153-157.
[49] Hua Fu, Dan Zhao, Xiao Lv. Optimized LS_SVM predictive control of gas outburstbased on improved PSO[A].2009International Conference on Information Engineeringand Computer Science[C].2009.
[50]李树刚,刘志云.综采面矿山压力与瓦斯涌出监测研究[J].矿山压力与顶板管理,2002,(1):99-101.
[51]戴广龙,汪有清,张纯.保护层开采工作面瓦斯涌出量预测[J].煤炭学报,2007,32(4):382-385.
[52]谢生荣,赵耀江.综采工作面的瓦斯涌出规律及涌出量的预测[J].太原理工大学学报.2005,36(5):553-556.
[53]杨东.采动落煤瓦斯涌出量预测模型的建立及应用[J].煤矿安全,2008,26(10):15-17.
[54]徐德金,胡宝林.基于GIS技术的瓦斯涌出量预测[J].煤矿安全,2009,35(5):94-96.
[55]都锋,刘恩,仇海生,等.回归分析法在预测瓦斯涌出量中的应用[J].煤矿安全,2010,(3):26-27.
[56]郭德勇,郑茂杰,鞠传磊,等.采煤工作面瓦斯涌出量预测逐步回归方法[J].北京科技大学学报,2009,31(9):1095-1097.
[57]蔡武,袁莎莎,戚洋.基于多元线性回归分析法预测矿井瓦斯涌出量[J].煤矿安全,2010,(1):91-94.
[58]伍爱友,田云丽,宋译,等.灰色系统理论在矿井瓦斯涌出量预测中的应用[J].煤炭学报,2005,30(5):389-392.
[59]陶云奇,许江,李树春.改进的灰色马尔柯夫模型预测采煤工作面瓦斯涌出量[J].煤炭学报,2007,32(4):391-395.
[60]詹海光,尹利平.采煤工作面瓦斯涌出量的聚类预测[J].矿业工程研究,2009,24(3):38-40.
[61]何华刚,胡敏涛.基于改进CC方法的瓦斯涌出混沌预测[J].矿业研究与开发,2009,29(4):69-71.
[62]赵红梅,陈开岩.R/S分形理论在瓦斯涌出量预测中的应用研究[J].山东煤炭科技,2010,(3):172-175.
[63]何俊,潘结南,聂百胜.瓦斯涌出严重程度分形预测研究[J].中国安全科学学报,2006,16(5):22-26.
[64] C. zgen, Karacan. Modeling and prediction of ventilation methane emissions ofU.S.longwall mines using supervised artificial neural networks[J]. International Journalof Coal Geology,2008,73:371-387.
[65]朱红青,常文杰,张彬.回采工作面瓦斯涌出BP神经网络分源预测模型及应用[J].煤炭学报,2007,32(5):504-507.
[66]施式亮,宋译,何利文.矿井掘进工作面瓦斯涌出混沌特性判别研究[J].煤炭学报,2006,31(6):702-705.
[67]黄为勇,童敏明,任子晖.基于SVM的瓦斯涌出量非线性组合预测方法[J].中国矿业大学学报,2009,38(2):235-238.
[68]马文涛.基于WT与GALSSVM的瓦斯涌出量预测[J].采矿与安全工程学报,2009,26(4):524-527.
[69]潘结南,陈江峰,徐文鹏. R/S分析及矿井瓦斯涌出量的分形预测[J].煤田地质与勘探,2001,(3):14-15.
[70] PAN Jie-nan, MENG Zhao-ping, LIU Ya-chuan. Grey Smoothing Model for PredictingMine Gas Emission[J]. Journal of China University of Mining&Technology,2003,13(1):76-78.
[71]王汉斌,郝仲熙,牛冲槐.瓦斯涌出规律非线性系统模型理论与应用研究[J].金属矿山,2006,(4):59-61.
[72]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):1666-1668.
[73]张朝举,邱进伟.基于Fuzzy时间序列模型矿井瓦斯涌出量预测研究[J].能源技术与管理,2006,(2):21-22.
[74]曾勇,吴财芳.矿井瓦斯涌出量预测的模糊分形神经网络研究[J].2004,34(2):62-64.
[75] XU Chang-kai, WANG Yao-cai, WANG Jun-wei. Research on Forecast Technology ofMine Gas Emission Based on Fuzzy Data Mining[J]. J. China Univ. of Mining&Tech.(English Edition),2004,14(2):174-178.
[76]孟海东,孙搏,司子稳,等.基于时间序列的矿井瓦斯涌出量预测方法[J].工矿自动化,2010,(12):24-26.
[77]徐刚,罗飞飞,邓绪彪.基于随机动态模型的矿井瓦斯涌出量预测研究[J].煤炭工程,2009,(8):79-82.
[78]何利文.煤矿回采工作面瓦斯涌出非线性特性分析及预测仿真理论研究[D].长沙:中南大学,2009.
[79]黄炜伟,王强,卢爽,等.基于混沌时间序列的神经网络对瓦斯涌出量预测[J].煤,2008,14(5):7-9.
[80] Jing Hong, Yi Zhao. The study of the gas emission prediction model based onfuzzy-rough set neural network[A]. Proceedings of the2nd International Conference onMachine Learning and Computing[C].2010.
[81] Yumin Pan, Xiaoyu Zhang, Pengqian Xue. The amount prediction of gas emitted viawavelet neural network with grey correlation analysis[A]. Proceedings2010SixthInternational Conference on Natural Computation[C].2010.
[82] Xiang Chen, Weihua Cai. Wavelet neural net based work face gas emissionprediction[A].2009IEEE International Conference on Intelligent Computing andIntelligent Systems[C].2009.
[83] Ning Yuncai, Chen Xiang. Coal face gas emission prediction based on support vectormachine[A]. Proceedings of the2009International Conference on Artificial Intelligenceand Computational Intelligence[C].2009.
[84] Wang Jianping, Huang Hongxia, Li Hongbiao, et al. Prediction of mine gas emissionrate based on the SVM model[A].2008Chinese Control Conference[C].2008.
[85] Wang Changlong, Fu Weihua. Gas emission rate prediction in fully-mechanizedexcavated faces based on support vector machine[A]. Proceedings of the20093rdInternational Symposium on Intelligent Information Technology Application Workshops
[C].2009.
[86]邵良杉.基于粗糙集理论的煤矿瓦斯预测技术[J].煤炭学报,2009,34(3):371-374.
[87] SHAO Liang-shan, FU Gui-xiang. Disaster prediction of coal mine gas based on datamining [J]. Journal of Coal Science&Engineering (China),2008,(3).
[88]吕品,马云歌,周心权.上隅角瓦斯浓度动态预测模型的研究及应用[J].煤炭学报,2006,31(4):462-465.
[89]段霄鹏.基于商空间的煤矿瓦斯数据挖掘研究[D].太原:太原理工大学,2010.
[90]崔小彦,赵国强.混沌预测在井下瓦斯浓度监测的应用[J].辽宁工程技术大学学报（自然科学版）,2008,27(5):185-186.
[91]施式亮,何利文,宋译,等.基于混沌与神经网络辐合模型的回采工作面瓦斯涌出时序分析与预侧[A].中国职业安全健康协会2008年学术年会论文集[C].2008.
[92]张剑英,许徽,陈娟,等.基于粒子群优化的支持向量机在瓦斯浓度预测中的应用研究[J].工矿自动化,2010,(10):32-35.
[93]王其军,程久龙.基于免疫神经网络模型的瓦斯浓度智能预测[J].煤炭学报,2008,33(6):666-668.
[94]白峻尧.基于SVM的瓦斯涌出的自学习模型研究应用[D].西安:西安科技大学,2006.
[95]李苏旺.时间序列数据建模及其在瓦斯预测中的应用研究[D].太原:太原理工大学,2003.
[96]杨胜远,李金林,杨东朋. ARMA模型在瓦斯浓度预测中的应用[J].中国煤炭,2008,34(6):14-16.
[97]程健,白静宜,钱建生,等.基于混沌时间序列的煤矿瓦斯浓度短期预测[J].中国矿业大学学报,2008,37(2):231-234.
[98] Jian Cheng, Jian-sheng Qian, Guang-dong Niu, Yi-nan Guo. Gas concentrationforecasting based on support vector regression in correlation space via KPCA [A].Advances in Neural Networks.6th International Symposium on Neural Networks
[C].2009.
[99]杜晓坤.改进的混沌预测方法在瓦斯监测中的应用[J].微计算机信息,2010,26(11):190-191.
[100]朱宇.构造神经网络在瓦斯时间序列预测中的应用[D].太原:太原理工大学,2010.
[101]高莉,胡延军,于洪珍.基于W-RBF的瓦斯时间序列预测方法[D].煤炭学报,2008,33(1):68-70.
[102] GAO Li, YU Hong-zhen. Prediction of Gas Emission Based on Information Fusion andChaotic Time Series [J]. J. China Univ. of Mining&Tech.(English Edition),2006,16(1):94-96.
[103] Ma Xian-Min. Coal gas outburst predication and control based on RBF neural network
[A].20103rd IEEE International Conference on Computer Science and InformationTechnology[C].2010.
[104]赵金宪,于光华.瓦斯浓度预测的混沌时序RBF神经网络模型[J].黑龙江科技学院学报,2010,2(2):131-134.
[105] Jin-Xian Zhao, Hong-Zhang Jin, Guang-Hua Yu. Coalmine gas concentrationforecasting based on chaotic theory and neural network model [A]. Proceedings of theThird International Conference on Information and Computing Science[C].2010.
[106] Qu Zhiming, Liang Xiaoying. Application of grey relation clustering and CGNN in gasconcentration prediction in top corner of coal mine[A].2009International Conferenceon Computational Intelligence and Natural Computing[C].2009.
[107] Zhang Yueqin, Duan Xiaopeng. Based on Cluster Cover Algorithm in Coal Mine GasForecast Application[A]. Proceedings of the2010International Conference onIntelligent Computation Technology and Automation[C].2010.
[108] Yujun Liu, Yueqin Zhang, Yu Zhu. A constructive neural network learning method basedon quotient space and its application in coal mine gas prediction[A].2010InternationalConference on Intelligent Computing and Cognitive Informatics[C].2010.
[109] Yuxiang Shao, Dongmei Zhang. Mine forecast based on genetic annealing neuralnetwork[A]. Proceedings2009International Conference on Information Technology andComputer Science[C].2009.
[110]刘婷婷,史久根,韩江洪.基于SVM的瓦斯体积分数混沌时间序列预测[J].合肥工业大学学报（自然科学版）,2009,32(8):1150-1153.
[111] Xiao-hu Zhao, Ke-ke Zhao. Study of an improved online least squares support vectormachine algorithm and its application in gas prediction[A].2008Fourth InternationalConference on Natural Computation[C].2008.
[112] Xiao-hu ZHAO, Gang WANG, Ke-ke ZHAO, et al. On-line least squares support vectormachine algorithm in gas prediction [J]. Mining Science and Technology,2009,19(2):194-198.
[113]王泉夫,赵小虎,王刚.在线最小二乘支持向量机在瓦斯预测中的研究与应用[A].2010（沈阳）国际安全科学与技术学术研讨会论文集[C].2010.
[114] Jian-sheng Qian, Jian Cheng, Yi-nan Guo. A novel multiple support vector machinesarchitecture for chaotic time series prediction[A]. Advances in Natural Computation.Second International Conference[C].2006.
[115]许徽,张剑英,陈娟,等.基于分段线性方法的瓦斯浓度时间序列模式表示[J].工矿自动化,2010,(8):41-44.
[116]贾真.神经网络在煤矿瓦斯预警系统中的应用研究[D].长春:长春工业大学,2007.
[117] Xiaosheng Liu, Xiaobin Huang. Research on pre-warning expert system of coal minegas safety based on object-oriented [A].2008IEEE International Symposium onKnowledge Acquisition and Modeling Workshop[C].2008.
[118]熊廷伟.煤矿瓦斯爆炸预警技术研究[D].重庆:重庆大学,2005.
[119]牛聚粉.基于Mapx的煤与瓦斯预测预警技术研究[D].北京:中国地质大学,2006.
[120]李华.矿井掘进瓦斯爆炸实时智能预警监控系统[D].西安:西安科技大学,2005.
[121] Jian-sheng QIAN, Hong-sheng YIN, Xiu-rong LIU, et al. Data Processing Model ofCoalmine Gas Early-Warning System. Journal of China University of Mining andTechnology,2007,17(10):20-24.
[122] Xian-Min Ma. Application of data fusion theory in coal gas fire prediction system[A].2008International Conference on Intelligent Computation Technology and Automation[C].2008.
[123] Jipeng Lin. Pre-alarm System Of Coal Mine Based On ICA and Kalman Filter[A].Proceedings2010Sixth International Conference on Natural Computation[C].2010.
[124] Xie Guo-min, Fu Hua, Wang Shi-yun. Study On Early Warning Of Gas In Coal MineBased On Extension Data Mining [A].20102nd International Conference on EducationTechnology and Computer[C].2010.
[125] Jin-xing Song, Yu-fang Liu. Study on the mine gas disaster prevention system by GIS
[A].20102nd Conference on Environmental Science and Information ApplicationTechnology[C].2010.
[126]高莉,胡延军,于洪珍.基于W-RBF的瓦斯时间序列预测方法[D].煤炭学报,2008,33(1):68-70.
[127] GAO Li, YU Hong-zhen. Prediction of Gas Emission Based on Information Fusion andChaotic Time Series [J]. J. China Univ. of Mining&Tech.(English Edition),2006,16(1):94-96.
[128]施式亮,何利文,宋译,等.基于混沌与神经网络辐合模型的回采工作面瓦斯涌出时序分析与预侧[A].中国职业安全健康协会2008年学术年会论文集[C].2008.
[129]王其军,程久龙.基于免疫神经网络模型的瓦斯浓度智能预测[J].煤炭学报,2008,33(6):666-668.
[130] Yuxiang Shao, Dongmei Zhang. Mine forecast based on genetic annealing neuralnetwork[A]. Proceedings2009International Conference on Information Technology andComputer Science[C].2009.
[131]朱宇.构造神经网络在瓦斯时间序列预测中的应用[D].太原:太原理工大学,2010.
[132]白峻尧.基于SVM的瓦斯涌出的自学习模型研究应用[D].西安:西安科技大学,2006.
[133] Jian-sheng Qian, Jian Cheng, Yi-nan Guo. A novel multiple support vector machinesarchitecture for chaotic time series prediction[A]. Advances in Natural Computation.Second International Conference[C].2006.
[134] Jian Cheng, Jian-sheng Qian, Guang-dong Niu, et al. Gas concentration forecastingbased on support vector regression in correlation space via KPCA [A]. Advances inNeural Networks.6th International Symposium on Neural Networks [C].2009.
[135]王泉夫,赵小虎,王刚.在线最小二乘支持向量机在瓦斯预测中的研究与应用[A].2010（沈阳）国际安全科学与技术学术研讨会论文集[C].2010.
[136] Xiao-hu ZHAO, Gang WANG, Ke-ke ZHAO, et al. On-line least squares support vectormachine algorithm in gas prediction [J]. Mining Science and Technology,2009,19(2):194-198.
[137] Xiao-hu Zhao, Ke-ke Zhao. Study of an improved online least squares support vectormachine algorithm and its application in gas prediction[A].2008Fourth InternationalConference on Natural Computation[C].2008.
[138]崔小彦,赵国强.混沌预测在井下瓦斯浓度监测的应用[J].辽宁工程技术大学学报（自然科学版）,2008,27(5):185-186.
[139]钟震宇.小煤矿通风瓦斯集群监控系统的研究[D].武汉:中国地质大学,2006.
[140]杨禹华,钟震宇,蔡康旭.基于模糊模式识别的瓦斯含量指标异常预警技术[J].中国安全科学学报,2007,17(9):172-175.
[141] Shu-gang CAO, Yan-bao LIU, Yan-ping WANG. A forecasting and forewarning modelfor methane hazard in working face of coal mine based on LS-SVM[J]. Journal of ChinaUniversity of Mining and Technology,2008,18(2):172-176.
[142]黄为勇.基于支持向量机数据融合的矿井瓦斯预警技术研究[D].徐州:中国矿业大学,2009.
[143]邵良杉,付贵祥.基于数据融合理论的煤矿瓦斯动态预测技术[J].煤炭学报,2008,33(5):551-554.
[144]李骥平.商空间理论在煤矿预警中的应用[D].太原:太原理工大学,2010.
[145]张棋胜.云计算平台监控系统的研究与应用[D].北京:北京交通大学,2011.
[146]张玄.基于云计算的软件资源服务平台研究[D].上海:东华大学,2012.
[147]孙伟龙.基于IaaS云计算的Web应用技术研究[D].南京:南京理工大学,2011.
[148]刘枫.基于Google与计算平台的Web应用系统设计及实现[D].成都:电子科技大学,2011.
[149]王柳峰.基于虚拟化的云计算平台内存资源协同共享技术研究[D].长沙:国防科学技术大学,2011.
[150]刘永波,代君.云计算环境下的信息资源整合技术研究[J].计算机光盘软件与应用,2012,(8):24-25.
[151]刘秀.云计算中的数据管理关键技术及其应用[J].信息与电脑,2012,(4):121-122.
[152]冷鹏.基于云计算的信息管理系统的研究[J].制造业自动化,2012,34(6):7-9.
[153]李桂银.基于云计算的信息共享空间资源建设[J].图书馆现代化技术,2011,32(2):53-55.
[154]张杰.神华宁煤集团安全智能分析平台建设构想[J].工矿自动化,2012,(9):83-86.
[155]张杰.物联网技术在神华宁煤集团数字化矿山建设中的应用研究[J].煤矿现代化,2012,(5):60-63.
[156]程宽,臧东辉,张国锋.基于云计算的煤矿安全监管信息系统[J].应用能源技术,2012,(4):10-12.
[157]郝雪梅.基于云计算的煤炭企业信息化研究[D].太原:山西财经大学,2011.
[158]杨叔子,吴雅,轩建平,等.时间序列分析的工程应用（第二版）[M].武汉:华中科技大学出版社,2007.
[159]施仁杰,卢科学.时间序列分析引论[M].西安:西安电子科技大学出版社,1988.
[160]杨位钦,顾岚.时间序列分析与动态数据建模[M].北京:北京工业学院出版社,1989.
[161]王沁.时间序列分析及其应用[M].成都:西南交通大学出版社,2008.
[162] R.A.Davis. Introduction to time series and forecasting[M]. Beijing: Post&TelecomPress,2009.
[163]杨胜远,李金林,杨东朋. ARMA模型在瓦斯浓度预测中的应用[J].中国煤炭,2008,34(6):14~16.
[164]陶剑文,王士同.局部保留最大信息差v-支持向量机[J].自动化学报,2012,38(1):98-103.
[165]吴奇,严洪森.基于混沌v-支持矢量机的产品销售预测模型[J].机械工程学报,2010,46(7):128-136.
[166]范玉妹,郭春静.支持向量机算法的研究及其实现[J].河北工程大学学报（自然科学版）,2010,27(4):106-110.
[167]李向军,庄彬,孟志青.基于v-SVM的时态周期模式发现[J].微电子学与计算机,2009,26(11):169-176.
[168]刘华富,王仲.核函数对v-支持向量机的泛化能力影响分析[J].计算机工程与科学,2007,29(7):76-79.
[169] GAO Cheng-Feng, CHEN Tian-Lun, NAN Tian-Shi. Discussion of Some ProblemsAbout Nonlinear Time Series Prediction Using v-Support Vector Machine[J].Communications in Theoretical Physics,2010,(7):117-124.
[170] TAO JianWen, CHUNG FuLai, WANG ShiTong. A kernel learning framework fordomain adaptation learning [J]. Science China (Information Sciences),2012(9):1985-1920.
[171] WU Jing-min, ZUO Hong-fu, CHEN Yong. An estimation method for directmaintenance cost of aircraft components based on particle swarm optimization withimmunity algorithm [J]. Journal of Central South University of Technology (EnglishEdition),2005,21(2):95-101.
[172]王淑芳. v参数SVM在烟气轮机故障诊断中的应用[J].现代电子技术,2012,35(5):43-51.
[173]李益国,沈炯.基于v-支持向量机的边际电价预测及置信区间估计[J].东南大学学报（自然科学版）,2007,37(1):70-75.