煤与瓦斯突出前兆的非线性预测及支持向量机识别研究
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摘要
煤矿瓦斯监测监控系统已基本成形,但是缺乏对事态发展发生的前期预警功能。本文利用煤矿瓦斯监控系统监测的瓦斯浓度数据信息来研究煤与瓦斯突出的发生、发展及其动力学等非线性特征,以及基于支持向量机的煤与瓦斯突出及非突出的预测。主要创新性成果体现在如下几个方面:
1)分别从能量和动量守恒的角度,建立了煤与瓦斯突出的尖点突变模型,得出了突出的突变机理和发生条件,从而为煤与瓦斯突出灾害预测与防止提供了新的理论依据。
2)分析了煤巷掘进工作面瓦斯涌出量的影响因素。理论和监测表明:工作面瓦斯涌出量动态,与工作面前方的突出危险性存在着较好的一致性,突出前工作面瓦斯涌出量变化异常;而非突出时瓦斯浓度比较均匀且较小。指出了工作面瓦斯涌出量动态特征是一项较好的非接触式预测指标,具有不占用作业时间、不施工预测钻孔,能连续动态预测的优势,能弥补现在的瓦斯监测监控系统不能预测煤与瓦斯突出的缺陷。
3)运用混沌动力学理论,研究了突出发生前与不发生突出的对比混沌运动特征。计算了煤巷掘进工作面瓦斯涌出量的Hurst指数,其Hurst指数都小于0.5,表明具有更强的突变性。采用复自相关函数法计算了相空间的延迟时间,发现了突出前瓦斯浓度的相空间延迟时间不小于不发生突出的相空间延迟时间。指出了传统计算关联维方法的缺陷,提出了适用于高维混沌系统的新的关联维算法。发现了突出前瓦斯浓度的关联维都大于不发生突出的;而最小饱和嵌入维却正好相反。改进了考虑到较小演化向量长度及较小的演化角度要求的Wolf算法,发现了突出前瓦斯浓度lyapunov指数的最大值都大于不发生突出的规律。
4)指出了局域预测法中的不足是最邻近点与中心点的关联程度,提出了计算最邻近点的新方法,能有效地防止产生伪邻近点。指出了Lyapunov预报模式预测值的正负取舍对瓦斯浓度预报的整体精度有较大影响,提出了Lyapunov预报模式预测值的判定思路,并且也提出了相应的判定算法。提出了基于小波与混沌集成的混沌时间序列的预测方法。本文改进的加权一阶局域预测法、改进的Lyapunov指数模式预测法及基于小波与混沌集成的混沌预测法,并且具有相当高的精度。
5)提出了利用支持向量机来识别掘进工作面监测信息中的突出危险性。研究了突出危险性模式识别的核函数构造原理和算法。得出了煤与瓦斯突出及非突出的掘进工作面的监测瓦斯浓度的时域特征向量、频域特征向量、小波域特征向量、分形与混沌特征向量。而且支持向量机能应用在常规煤与瓦斯突出预测和KBD7煤岩动力灾害电磁辐射监测仪的数据处理。结果表明:支持向量机模型识别器解决了小样本、非线性、高维数、局部极小值等实际问题,具有良好的分类识别效果。
本文采用Visual C#2005和Matlab7.0语言,开发了煤与瓦斯突出前兆的支持向量机识别系统,与原有的煤矿瓦斯监测监控系统进行数据通信,实现连续非接触式地识别煤与瓦斯突出及非突出,预测结果是可信的,与实际是吻合的,具有重大的现实意义。
论文有图113幅,表19个,参考文献161篇。
The coal mine monitoring gas system has been established in lots of coal mines, but it is lack the early warning function to forecast coal and gas outburst. In this paper it is to study the development, dynamics, and other non-linear characteristics of the coal and gas outburst occurred by the coal mine monitoring gas system to monitor the gas concentration data, as well as to predict coal and gas outburst based on support vector machines. The main innovation results are as follows:
1) Not only the cusp catastrophe models of coal and gas outburst are established respectively from the point of view of energy and momentum conservation, but also the chop mechanisms and the conditions of coal and gas outburst are concluded. So the new theory of disaster prediction and prevention from coal and gas outburst is put forward.
2) The effect factors of gas emission from the working face have been analysed. The theory and monitoring data are indicated as follows: The gas emission trends, which are abnormity when the coal and gas outbust happens, while are uniformity and smallness when the coal and gas outbust does not take place from the working face, are consistency with the danger of coal and gas outburst in the working face. The dynamic characteristics of the gas emission from the working face are the better predictors of non-contact, which not only do not engross busywork time, but also do not broach forecast, then which have the advantage of continued and dynamic forecast,as well as make up the disfigurement which the coal mine monitoring gas system does not forecast coal and gas outburst.
3) The contrast features of chaotic motion between coal and gas outburst happening and coal and gas outburst not taking place, are studied by using the Chaos Kinetic theory. The Hurst exponent of gas emission from the working face is calculated. Its results are less than 0.5, which are indicated that it has strong chop characteristic of coal and gas outburst. The retardation time of phase space is calculated by using the complex correlative function method. The retardation time of phase space of gas emission from the working face in the coal and gas outburst happened is biger than that of phase space of gas emission from the working face in the coal and gas no outburst. The traditional method defects of calculating the correlation dimension are pointed out, while the new method of calculating the correlation dimension, which is applicable for high-dimensional chaotic system, is put forward. The correlation dimension of gas emission from the working face in the coal and gas outburst happened is biger than that of phase space of gas emission from the working face in the coal and gas no outburst, while the result of the least inbuilt saturation dimension is opposition. The Wolf arithmetic of Lyapunov exponent is ameliorated in view of the less evolutive vector length and angle, and it is found the law that the Lyapunov exponent of gas emission from the working face in the coal and gas outburst happened is greater than that of phase space of gas emission from the working face in no the coal and gas outburst.
4) It is pointed that the shortage of part forecast is rested with the correlative degree between the nearest point and the center point, The calculation of the nearest point of new method which can effectively prevent the neighboring pseudo-point, is put forward. The prediction model of Lyapunov , which the predictive value is positive and negative, is impacted on the overall accuracy of forecasting gas concentration. So, not only the Lyapunov of forecasting model to determine the predictive value of ideas, but also of determining the appropriate algorithm are put forward. Wavelet based on the integration of chaos and chaotic time series forecasting methods is also put forward. The very high precisions of gas concentration are forecasted dividably by improving weight of first-order local-region prediction method, by improving prediction model of Lyapunov, and by improving prediction models based on wavelet and chaotic integrated prediction of chaos.
5) It is pointed out that the information to monitor the heading face of the danger of coal and gas outburst is identified by support vector machines in the coal mine monitoring gas system. The kernel pattern recognition function and the structure principles of algorithm applying the heading face of the danger of coal and gas outburst are studied. The time-domain characteristics of the vector, the frequency characteristics of the vector, the wavelet domain feature vector, and fractal and chaos characteristics of the vector are educed in the gas concentration of the heading face of the coal and gas outburst and no-outburst by the coal mine monitoring gas system. And support vector machines are in the application of forecasting conventionally coal and gas outburst, and of processing data from disaster KBD7 coal-power electromagnetic radiation monitor. The results show that the support vector machines identifier model, which solves the real problems of the small sample, non-linear, high-dimension, the local minimum value, and so on, has a good classification results. In this paper, by using Visual C# 2005 and Matlab7.0 language, the support vector
machines recognition system, which distinguishes continually non-contactly coal and gas outburst from no outburst by analysing precursor information of coal mine, with the original coal mine gas monitoring system communicating the internet for data, is empoldered. The forecasting results are believable, are consistent with the mine practice, so the support vector machine recognition system is provided with of great practical significance.
1)分别从能量和动量守恒的角度,建立了煤与瓦斯突出的尖点突变模型,得出了突出的突变机理和发生条件,从而为煤与瓦斯突出灾害预测与防止提供了新的理论依据。
2)分析了煤巷掘进工作面瓦斯涌出量的影响因素。理论和监测表明:工作面瓦斯涌出量动态,与工作面前方的突出危险性存在着较好的一致性,突出前工作面瓦斯涌出量变化异常;而非突出时瓦斯浓度比较均匀且较小。指出了工作面瓦斯涌出量动态特征是一项较好的非接触式预测指标,具有不占用作业时间、不施工预测钻孔,能连续动态预测的优势,能弥补现在的瓦斯监测监控系统不能预测煤与瓦斯突出的缺陷。
3)运用混沌动力学理论,研究了突出发生前与不发生突出的对比混沌运动特征。计算了煤巷掘进工作面瓦斯涌出量的Hurst指数,其Hurst指数都小于0.5,表明具有更强的突变性。采用复自相关函数法计算了相空间的延迟时间,发现了突出前瓦斯浓度的相空间延迟时间不小于不发生突出的相空间延迟时间。指出了传统计算关联维方法的缺陷,提出了适用于高维混沌系统的新的关联维算法。发现了突出前瓦斯浓度的关联维都大于不发生突出的;而最小饱和嵌入维却正好相反。改进了考虑到较小演化向量长度及较小的演化角度要求的Wolf算法,发现了突出前瓦斯浓度lyapunov指数的最大值都大于不发生突出的规律。
4)指出了局域预测法中的不足是最邻近点与中心点的关联程度,提出了计算最邻近点的新方法,能有效地防止产生伪邻近点。指出了Lyapunov预报模式预测值的正负取舍对瓦斯浓度预报的整体精度有较大影响,提出了Lyapunov预报模式预测值的判定思路,并且也提出了相应的判定算法。提出了基于小波与混沌集成的混沌时间序列的预测方法。本文改进的加权一阶局域预测法、改进的Lyapunov指数模式预测法及基于小波与混沌集成的混沌预测法,并且具有相当高的精度。
5)提出了利用支持向量机来识别掘进工作面监测信息中的突出危险性。研究了突出危险性模式识别的核函数构造原理和算法。得出了煤与瓦斯突出及非突出的掘进工作面的监测瓦斯浓度的时域特征向量、频域特征向量、小波域特征向量、分形与混沌特征向量。而且支持向量机能应用在常规煤与瓦斯突出预测和KBD7煤岩动力灾害电磁辐射监测仪的数据处理。结果表明:支持向量机模型识别器解决了小样本、非线性、高维数、局部极小值等实际问题,具有良好的分类识别效果。
本文采用Visual C#2005和Matlab7.0语言,开发了煤与瓦斯突出前兆的支持向量机识别系统,与原有的煤矿瓦斯监测监控系统进行数据通信,实现连续非接触式地识别煤与瓦斯突出及非突出,预测结果是可信的,与实际是吻合的,具有重大的现实意义。
论文有图113幅,表19个,参考文献161篇。
The coal mine monitoring gas system has been established in lots of coal mines, but it is lack the early warning function to forecast coal and gas outburst. In this paper it is to study the development, dynamics, and other non-linear characteristics of the coal and gas outburst occurred by the coal mine monitoring gas system to monitor the gas concentration data, as well as to predict coal and gas outburst based on support vector machines. The main innovation results are as follows:
1) Not only the cusp catastrophe models of coal and gas outburst are established respectively from the point of view of energy and momentum conservation, but also the chop mechanisms and the conditions of coal and gas outburst are concluded. So the new theory of disaster prediction and prevention from coal and gas outburst is put forward.
2) The effect factors of gas emission from the working face have been analysed. The theory and monitoring data are indicated as follows: The gas emission trends, which are abnormity when the coal and gas outbust happens, while are uniformity and smallness when the coal and gas outbust does not take place from the working face, are consistency with the danger of coal and gas outburst in the working face. The dynamic characteristics of the gas emission from the working face are the better predictors of non-contact, which not only do not engross busywork time, but also do not broach forecast, then which have the advantage of continued and dynamic forecast,as well as make up the disfigurement which the coal mine monitoring gas system does not forecast coal and gas outburst.
3) The contrast features of chaotic motion between coal and gas outburst happening and coal and gas outburst not taking place, are studied by using the Chaos Kinetic theory. The Hurst exponent of gas emission from the working face is calculated. Its results are less than 0.5, which are indicated that it has strong chop characteristic of coal and gas outburst. The retardation time of phase space is calculated by using the complex correlative function method. The retardation time of phase space of gas emission from the working face in the coal and gas outburst happened is biger than that of phase space of gas emission from the working face in the coal and gas no outburst. The traditional method defects of calculating the correlation dimension are pointed out, while the new method of calculating the correlation dimension, which is applicable for high-dimensional chaotic system, is put forward. The correlation dimension of gas emission from the working face in the coal and gas outburst happened is biger than that of phase space of gas emission from the working face in the coal and gas no outburst, while the result of the least inbuilt saturation dimension is opposition. The Wolf arithmetic of Lyapunov exponent is ameliorated in view of the less evolutive vector length and angle, and it is found the law that the Lyapunov exponent of gas emission from the working face in the coal and gas outburst happened is greater than that of phase space of gas emission from the working face in no the coal and gas outburst.
4) It is pointed that the shortage of part forecast is rested with the correlative degree between the nearest point and the center point, The calculation of the nearest point of new method which can effectively prevent the neighboring pseudo-point, is put forward. The prediction model of Lyapunov , which the predictive value is positive and negative, is impacted on the overall accuracy of forecasting gas concentration. So, not only the Lyapunov of forecasting model to determine the predictive value of ideas, but also of determining the appropriate algorithm are put forward. Wavelet based on the integration of chaos and chaotic time series forecasting methods is also put forward. The very high precisions of gas concentration are forecasted dividably by improving weight of first-order local-region prediction method, by improving prediction model of Lyapunov, and by improving prediction models based on wavelet and chaotic integrated prediction of chaos.
5) It is pointed out that the information to monitor the heading face of the danger of coal and gas outburst is identified by support vector machines in the coal mine monitoring gas system. The kernel pattern recognition function and the structure principles of algorithm applying the heading face of the danger of coal and gas outburst are studied. The time-domain characteristics of the vector, the frequency characteristics of the vector, the wavelet domain feature vector, and fractal and chaos characteristics of the vector are educed in the gas concentration of the heading face of the coal and gas outburst and no-outburst by the coal mine monitoring gas system. And support vector machines are in the application of forecasting conventionally coal and gas outburst, and of processing data from disaster KBD7 coal-power electromagnetic radiation monitor. The results show that the support vector machines identifier model, which solves the real problems of the small sample, non-linear, high-dimension, the local minimum value, and so on, has a good classification results. In this paper, by using Visual C# 2005 and Matlab7.0 language, the support vector
machines recognition system, which distinguishes continually non-contactly coal and gas outburst from no outburst by analysing precursor information of coal mine, with the original coal mine gas monitoring system communicating the internet for data, is empoldered. The forecasting results are believable, are consistent with the mine practice, so the support vector machine recognition system is provided with of great practical significance.
引文
[1]于不凡,王佑安.煤矿瓦斯灾害防治及利用技术手册[M].北京:煤炭工业出版社,2000,8
[2]李成武.煤与瓦斯突出危险状态灰色分类及预测技术研究[D].徐州:中国矿业大学出版社, 2005.4
[3]何学秋,王从银.从我国煤矿瓦斯灾害多发现象论安全科学基础研究的迫切性[J].中国科学基金,2002,5.
[4]吴中立.矿井通风与安全[M].徐州:中国矿业大学出版社,1988.
[5]邓聚龙.灰色控制系统[M].华中理工大学出版社,1997.10.
[6]刘思峰,郭天榜等.灰色系统理论及其应用[M].北京:科学出版社,1991.
[7]邓聚龙.灰色系统基本方法[M].武昌:华中工学院出版社,1987.
[8]邓聚龙.灰色系统理论教程[M].武昌:华中理工大学出版社,1989.
[9] [苏联]B.B.霍多特著,宋士钊,王佑安译.煤与瓦斯突出[M].北京:中国工业出版社,1966,5.
[10]马尚权,王恩元,何学秋等.煤与瓦斯突出预测预报方法[J].矿山机械,2001(5):71-72
[11]唐春安,徐小荷.岩石破裂过程失稳的尖点突变模型[J]岩石力学与工程学报,1990(2):100-107
[12]唐春安等.岩石破裂过程数值实验[M].北京科学出版社,2003
[13]章梦涛.冲击地压失稳理论与数值模拟计算[J].岩石力学与工程学报,1987(3):197-204
[14]章梦涛,徐曾和,潘一山.冲击地压和突出的统一失稳理论[J],煤炭学报,1991(4):48一53
[15]潘一山,章梦涛.冲击地压失稳理论的解析分析[J].岩石力学与工程学报,1996,Vol.15: 504-510
[16]徐曾和,章梦涛.冲击地压失稳理论及其应用[C].第二届全国岩石动力学学术会议论文集,宜昌,1990,10:332-339
[17]苗琦,杨胜强,欧晓英,陈祖云.煤与瓦斯突出灰色-神经网络预测模型的建立及应用[J].采矿与安全工程学报,2008,25,(3):309-312.
[18]虞和济.基于神经网络的智能诊断[M].北京:冶金工业出版社,2000.5
[19]戴葵.神经网络实现技术[M].长沙:国防科技大学出版社,1998
[20]由伟,刘亚秀,李永.用人工神经网络预测煤瓦斯突出[J].煤炭学报[J],1007,32,(3):385-387.
[21] John Grznar, Sameer Prasad, Jasmine Tata,.Neural networks and organizational systems: Modeling non-linear relationships[J], European Journal of Operational Research, Volume 181, Issue 2, 1 September 2007, 939-955.
[22] Manoj Khandelwal, T.N. Singh. Prediction of blast induced ground vibrations and frequency in opencast mine: A neural network approach[J]. Journal of Sound and Vibration, Volume 289, Issues 4-5, 7 February 2006, 711-725.
[23] K. Shanti Swarup, G. Sudhakar. Neural network approach to contingency screening and ranking in power systems[J], Neurocomputing, Volume 70, Issues 1-3, December 2006, 105-118.
[24] J.C. Cao, S.H. Cao. Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis[J]. Energy, Volume 31, Issue 15, December 2006, 3435-3445.
[25]聂百胜,何学秋,王恩元,刘贞堂等.煤与瓦斯突出预测技术研究现状及发展趋势[J].中国安全科学学报,Vol.13,2003(6).:40-43.
[26]H.F. Janas, E. Kunz. Evaluation of the danger of coal-gas outbursts based on gas pressure and permeability[J]. Fuel and Energy Abstracts Volume 37, Issue 3, May 1996,220.
[27]付建华,程远平.中国煤矿煤与瓦斯突出现状及防治对策[J].采矿与安全工程学报,2007.34(3): 253-259.
[28]吴财芳,曾勇.基于专家系统的煤与瓦斯突出预测知识库研究[J].煤田地质与勘探, 2004(2): 17-20.
[29]张小东,张子戌,秦勇.基于专家系统的煤与瓦斯突出区域预测研究[J].煤矿安全,2004(9): 1-3.
[30]蔡成功,张建国.煤与瓦靳突出规律的分析探讨[J].煤矿安全,2003(12):3-6.
[31]王英敏译.煤与瓦斯突出概论[M].北京:煤炭工业出版社.1958.5.
[32]中国矿业大学瓦斯组编.煤与瓦斯突出的防治[M].北京:煤炭工业出版社.1979.3
[33] A.T.艾鲁尼著.唐修义,宋德淑,王荣龙译.煤与瓦斯动力现象的预测与防治[M].北京:煤炭工业出版社.1992.9
[34]周世宁,林柏泉.煤层瓦斯赋存与流动理论[M].北京:煤炭工业出版社.1999.2:5-7.
[35]程伟.煤与瓦斯突出危险性预测与防治技术[M].徐州:中国矿业大学出版社,2003.7.
[36]何学秋.含瓦斯煤岩流变动力学[M].徐州:中国矿业大学出版社,1995.5.
[37]何学秋,刘明举.含瓦斯煤岩破坏电磁动力学[M].徐州:中国矿业大学出版社,1995.5.
[38]王恩元,何学秋,聂百胜,刘贞堂.电磁辐射法预测煤与瓦斯突出原理[J].中国矿业大学学报, Vol29,3,2000(5):225-229.
[39]撒占友,何学秋,王恩元.工作面煤与瓦斯突出电磁辐射的神经网络预测方法研究[J],煤炭学报,Vol29,5,2004(10):563-567.
[40]窦林名,何学秋,王恩元.冲击矿压预测的电磁辐射技术及应用,煤炭学报, Vol29,4, 2004(8):396-399.
[41]何学秋,王恩元,聂百胜等.煤岩流变电磁动力学[M].北京:煤炭工业出版社,2003.4.
[42]于不凡.国内外煤与瓦斯突出日常预测研究综述[M].煤炭科学总院重庆分院编.煤与瓦斯突出预测资料汇编,1987.
[43]中华人民共和国煤炭部.防治煤与瓦斯突出细则[M].北京:煤炭工业出版社,1995.
[44]王凯,俞启香.煤与瓦斯突出的非线性特征及预测模型[M].徐州:中国矿业大学出版社,2005,3
[45]工作面钻孔瓦斯涌出初速度特征分析[D].硕士学位论文.抚顺:煤炭科学研究总院抚顺分院,1985.
[46]王克全.突出煤层钻孔瓦斯涌出特点研究[D].硕士学位论文.重庆:煤炭科学研究总院重庆分院, 1986.
[47]胡千庭.WTC瓦斯突出仪及其应用[J].煤炭工程师,1994(6):2-6
[48]苏文叔.利用瓦斯涌出动态指标预测煤与瓦斯突出[J].煤炭工程师,1996(5):2-7
[49]南存全,冯夏庭.基于模式识别的煤与瓦斯突出区域预测[J].东北大学学报(自然科学版), 2004,25(9):903-906.
[50]周骏,曲云尧,周文涛,汤成.煤与瓦斯突出模式识别预测软件的设计原理[J].山东矿业学院学报,1996,15(1):61-66.
[51] Li S,Zhang H. Coal and gas outburst model recognition and regional prediction.In:Proceedings in Mining Science and Safety Technology.Beijing:Science Publishing House,2002.33
[52]张宏伟,李胜.煤与瓦斯突出危险性的模式识别和概率预测[J],岩石力学与工程学报, 2005,19(10):3577-3581.
[53]刘明举.计算机模式识别技术在煤与瓦斯突出预测中的应用[J].瓦斯地质,1989(12).
[54]王显政.煤矿安全新技术[M].北京:煤炭工业出版社,2002.
[55]魏建平.矿井动力灾害煤岩电磁辐射预警机理及其应用研究[D].徐州:中国矿业大学博士论文, 2004.
[56]李忠辉,王恩元,何学秋,刘贞堂,宋霞女.电磁辐射实时监测煤与瓦斯突出在煤矿的应用[J].煤炭科学技术,2005(9):31-33.
[57]王佑安,王魁军.工作面突出危险性预测敏感指标确定方法探讨[J],煤矿安全,1991(10).9-14
[58]杨光正,吴岷,张晓莉.模式识别[M].合肥:中国科学技术出版社,2001,7.
[59]吴逸飞译.模式识别-原理、方法及应用[M].北京:清华大学出版社,2002,11.
[60]边肇祺,张学工.模式识别[M].北京:清华大学出版社,2002,2
[61] C .J. Burges. A Tutorial on Support Vector Machines for Patern Recognition[J]. Data Mining and Knowledge Discovery.1998,2(2):121-16
[62]K. Balakrishnan, V. Honavar. Intelligent Diagnosis Systems. Improving Design of Intelligent Systems: Outstanding Problems and Some Methods for their Solution[J]. A Special Issue of the International Journal of Intelligent Systems. 1998, 8(3/4):239-290
[63] Schwutke U.M.,Veregge J.R.,etal. Cooperating Expert System for the Next Generation of Real Time Monitoring Application.IEEE 2th International Conferenceon Expert System for Develop. 1994: 210-214
[64] Hoskins J.C.,Kaliyu K.M. Fault Diagnosisin Complex Chemical Plants Using A rtificial Neural Network[J]. ArtificialIn telligence.1991,37(1):137-141
[65]何学秋.含瓦斯煤岩流变动力学[M].徐州:中国矿业大学出版社,1995.5
[66]蒋承林,俞启香.煤与瓦斯突出的球壳失稳机理及防治技术[M],徐州:中国矿业大学出版社,1998:114-116
[67]张铁岗.矿井瓦斯综合治理技术[M].北京:煤炭工业出版社,2001
[68]王凯.钻孔法研究预测煤与瓦斯突出的研究[D].博士论文.徐州:中国矿业大学出版社,1997
[69]吕绍林,何继善.瓦斯突出煤体的粒度分形研究[J].煤炭科学技术, 1999, 27(2): 46-48
[70]王凯,俞启香.非线性理论在煤与瓦斯突出研究中的应用[J].辽宁工程技术大学学报,1998,8(6):348-352
[71]王凯,俞启香.煤与瓦斯突出起动过程的突变理论研究[J].中国安全科学学报, 1998, 8(6):10-15.
[72]王轶波.煤巷掘进工作面瓦斯涌出的非线性特征及突出预测研究[D].硕士论文.徐州:中国矿业大学出版社,2004.
[73]李树刚.综放开采围岩活动及瓦斯运移[M].徐州:中国矿业大学出版社,2000.5.
[74] [苏联]O.H切尔诺夫,E.C罗赞采失著,宋士钊,于不凡译.瓦斯突出危险煤层井田的准备[M].北京:煤炭工业出版社,1980,8.
[75] [苏联]A.A.斯阔成基编,Γ.д.李金著,王英敏译.矿井瓦斯涌出量研究[M].北京:煤炭工业出版社,1958,5.
[76]于不凡,王佑安.煤矿瓦斯灾害防治及利用技术手册[M].北京:煤炭工业出版社,2000,8.
[77] Andrew R.Webb著,王萍,杨培龙,罗颖昕译.统计模式识别Statistical Pattern Recognition[M].北京:电子工业出版社,2004,10.
[78]Jain,A.K.,Duin, R.P.W.and Mao,J.(2000) Statistical pattern recognition:A revew[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,22(1):4-37
[79]孙即祥.现代模式识别[M].长沙:国防科技大学出版社,2002,1
[80]肖健华,吴今培.基于支持向量机的模式识别方法.五邑大学学报(自然科学版).第l6卷第1期,2002年3月:6-10
[81] Dominique M,Alistair B.Nonlinear blind source separation using kernels[J].IEEE Tram.On Neural Networks,2003,14(1):228—235.
[82] Mike Fugate,James R,Gattikers.Computer Intrusion Detection with Classification and Anomaly Detection Using SVMs[C].International Journal of Pattern Recognition and Artificial Intelligence,2003,17(3):441—458.
[83] P.T.桑德斯著.凌复华译.突变理论入门[M].上海;上海科学技术文献出版,1983.
[84]凌复华.突变理论及应用[M].上海:上海交通大学出版社,1985.
[85]王连国,缪协兴.基于尖点突变模型的矿柱失稳机理研究[J].采矿与安全工程学报, VoI.23 No.2, 2006(2):137-140.
[86]章梦涛,徐曾和,潘一山等.冲击地压和突出的统一失稳理论[J].煤炭学报1991.4:48—52
[87]郭文兵,邓喀中,邹友峰.条带煤粒的突变破坏换稳理论研究[J].中国矿业大学学报,2005,1: 77—80.
[89]马中飞,俞启香.煤与瓦斯承压散体失控突出机理的初步研究[J],煤炭学报2006.3:299—333.
[90]肖福坤,秦宪礼,张娟等.煤与瓦斯突出过程的突变分析[J],辽宁工程技术大学学报,2004.4: 442-444
[91]李玉,赵国景.煤层突出的突变模式[J],北京科技大学学报1995.1:5-9.
[92]勾攀峰,汪成兵,韦四洒.基于突变理论的深井巷道临界深度[J].岩石力学与工程学报,2004.24: 4237-4141.
[93]潘岳,张孝伍.狭窄煤柱岩爆的突变理论分析[J],岩石力学与工程学报2004.12:1797—1803.
[94]尹光志,李贺,鲜学福等.煤岩体失稳的理论模型[J],重庆大学学报1994.1:23—28.
[95]刘保县,鲜学福,刘新荣等.爆破激发煤瓦斯突出的研究[J],中国矿业2000.3:89—91
[96]王凯,俞启香.煤与瓦斯突出起动过程的突变理论研究[J],中国安全科学学报1998.6:10—15
[97]李晓伟,蒋承林,初始释放瓦斯膨胀能与煤体破碎程度的关系研究[J].煤矿安全,2008,5:1-3
[98]方健之,俞善炳,谈庆明.煤与瓦斯突出的层裂—粉碎模型[J],煤炭学报.1995.2:149-152.
[99]俞善炳.恒稳推进的煤与瓦斯突出[J],力学学报1988.2:97-106.
[100]俞善炳.煤与瓦斯突出的一维流动模型和启动判据[J]力学学报1992.4:418-431.
[101]周世宁.瓦斯在煤层中流动的机理[J].煤炭学报,V01.15 No.1, 1990(3):15-24.
[102]张广洋,谭学术,鲜学福.煤层瓦斯运移的数学模型[J].重庆大学学报,1994(4):53-57.
[103]科学技术文献出版社重庆分社[M].国外煤与瓦斯突出(第一集).1978.7:34-49.
[104]国家安全生产监督管理总局.低瓦斯煤矿也要安装瓦斯监测系统[J].煤炭企业管理,2006,4:9
[105]聂韧,赵旭生.掘进工作面瓦斯涌出动态指标预测突出危险性的探讨,矿业安全与环保,2004 (8):36-38.
[106]苏文叔.利用瓦斯涌出动态指标预测煤与瓦斯突出[J].煤炭工程师,1996(5):2-7.
[107]秦汝祥,张国枢,杨应迪.瓦斯涌出异常预报煤与瓦斯突出[J],煤炭学报,2006(10):599-602.
[108]李秋林.应用瓦斯涌出特征实施突出预报的初步探索[J],矿业安全与环保,2006(2):68-70.
[109]庄新田,庄新路,田莹.Hurst指数及股市的分形结构[J].东北大学学报(自然科学版),2003(9): 862-864
[110]张利平,王德智,夏军,牛存稳.R/S分析在洪水变化趋势预测中的应用研究[J].中国农村水利水电, 2005年第2期:38-40
[111] Hurst指数估算中存在的若干问题.地质科学,2004(2):177-182.
[112] F. LEPRETI, P. C. FANELLO, F. ZACCARO, V. CARBONE. Persistence of solar activity on small scales: Hurst analysis of time series coming from Hαflares[J]. Solar Physics, Volume 197, 2000(11) : 149-156.
[113] Peter Hall, Wolfgang H?rdle, Torsten Kleinow, Peter Schmidt.Semiparametric Bootstrap Approach to Hypothesis Tests and Confidence Intervals for the Hurst Coefficient[J]. Statistical Inference for Stochastic Processes, Volume 3, 2000(10):263-276.
[114] G. A. Sviridyuk, A. A. Zamyshlyaeva. The Phase Spaces of a Class of Linear Higher-Order SobolevType Equations[J]. DIFFERENTIAL EQUATIONS, Vol.42 No.2, 2006:296-278.
[115]刘树勇,朱石坚,俞翔.相空间重构优化研究,数据采集与处理:2008(1):65-68.
[116]林嘉宇,王跃科,黄芝平.语言信号相空间重构中的时间延迟的选择-复自相关法[J].信号处理, 1999,15(3):220-225.
[117]Liming W.,Robert Cawley.Smoothness implies determinism:A Method to Detect it in Time Series[J].Physical Review Letters,1994,73(8):1091-1094.
[118] Determination of Discrete Relaxation and Retardation Time Spectra from Dynamic Mechanical Data[J]. Rheologica Acta, Volume 28, Number 6, 1989(11):511-519.
[119] Jean-Charles, Robinet,Paul Sardini. The Effect of Rock Matrix Heterogeneities Near Fracture Walls on the Residence Time Distribution (RTD) of Solutes[J]. Transport in Porous Media, Volume 72, Number 3 , 2008(4):393-408.
[120] Mera M. ,Moran M. Degrees of freedom of a time Serirs [J]. Joural of Statistical Physics, 2002, Vol.106 (12):125-145.
[121]任志军,田心.高维混沌的研究现状与展望[J].国外医学生物学工程分册,2004,27(1):18-21.
[122] Wolf A. Determing Lyapunov Exponent from a Time Series[J].Physics, 1985,16D(3):285-317.
[123] Rosentein M.T. ,Collins J.J. ,Deluca C.J. A Practical method for Calculating Largest Lyapunov Exponents from Small Data Sets[J].Physical D, 1993,(65):117-134.
[124]陈士华,陆君安.混沌动力学初步.武汉:武汉水利电力大学出版社,1998.8.
[125]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用.武汉:武汉水利电力大学出版社,2002.4.
[126]王俊国,周建中,付波等.基于混沌时间序列的电力负荷短期预测方法[J].水电能科学, 2006.4: 70-72.
[127]岳毅宏,韩文秀,王健.基于加权平均一阶发散度的混沌序列预测法[J].系数工程与电子技术, 2004.5: 40-42.
[128]林振山.长期预报的相空间理论和模式[M].北京:气象出版社,1993.3.
[129]陈淑燕,王炜.基于Lyapunov指数的交通量混沌预测方法[J].土木工程学报,2004.37(9):96-99.
[130]蒋传文,侯志俭,李涛,张勇传.基于小波分解的径流非线性预测[J].上海交通大学学报, 2002(7):1053-1056.
[131]殷光伟,郑丕谔.基于小波与混沌集成的中国股票市场预测[J].系统工程学报, Vo1.20, No.2,2005(4):180-184.
[132]任霞.小波分析在电力系数中的应用[M].北京:中国电力出版社,2003.11.
[133]孔即祥.现代模式识别[M].长沙:国防科技大学出版社,2002.1.
[134]杨凌霄,沈鹰,侯国栋,王成硕.基于支持向量机的煤与瓦斯突出预测研究[J].河南理工大学学报,2006(10):348-250.
[135]齐永锋,火元莲.支持向量机研究[J].甘肃联合大学学报(自然科学版),2005(4):23-26
[136]肖汉光,蔡从中.利用支持向量机SVM识别车辆类型[J].重庆大学学报(自然科学版), 2006(1):61-64.
[137] James K.Model selection in kernel machine classification with applications in bioinformatics[D]. New Mexico:The University of New Mexico Albuquerque,Doctor of Philosophy Statistics,2005.5.
[138] Nello Cristianini,John Shawe-Taylor著.李国正,王猛,曾华军译.An Introduction to Support Vector Machines and Other Kernel-based Learning Methods.支持向量机导论[M].北京:电子工业出版社,2004.3.
[139] Deloste D. ,Scholkopf B. Training Invariant Support Vector Machines[J]. Machine Learing, 2002, 46(1-2-3):161-190.
[140]吴涛.核函数的性质、方法信其在障碍检测中的应用[D].长沙:国防科技大学出版社博士论文,2003.
[141]李忠辉,王恩元,何学秋,刘贞堂,宋霞女.电磁辐射实时监测煤与瓦斯突出在煤矿的应用.煤炭科学技术,2005(9):31-33.
[142]国家发明专利(ZL98111185.8)和实用新型专利(ZL01272808.X).
[143]何学秋,聂百胜,王恩元等.矿井煤岩动力灾害电磁辐射预警技术[J].煤炭学报,32(1),2007. 1:56-59.
[144]潘爱民.计算机网络(第四版),北京:清华大学出版社,2004.
[145]范建华等.TCP/IP详解.卷1:协议[M],北京:机械工业出版社,2000.
[146]陆雪莹.TCP/IP详解.卷2:实现[m],北京:机械工业出版社,2004.
[147]胡谷雨等.TCP/IP详解.卷3:TCP事务协议、HTTP、NNTP和UNIX域协议[M]北京:机械工业出版社,2000.
[148] H.M.Deitel,P.L.Deitel.著.刘文红译.Visual C#2005大学教程(第二版).北京:电子工业出版社, 2007.7.
[149]蒋承林.煤与瓦斯突出阵面推进过程及力学条件分析[J].中国矿业太学学报,1994.23(4): 1～9
[150]蒋承林.煤与瓦斯突出过程中能量耗散规律的研究[J].煤炭学报,1996,21(2):l73～178
[151]蒋承林.煤样的韧始释放瓦斯膨胀能测定[J].岩石力学与工程学报,1996,15(4):39～400
[152]蒋承林.石门揭穿含瓦斯煤层时动力现象的球壳失稳机理研究[D],[博上学位论文].徐州:中国矿业大学采矿工程系,1994
[153]蒋承林俞启香.煤与瓦斯突出机理的球壳失稳假说[J].煤矿安全,1995,2:16-25
[154]吴方同.数学物理方程[M].武汉:武汉大学出版社,2001.11
[155]赵振海.数学物理方程与特殊函数学习指导与习题全解[M].大连,大连理工大学出版社,2003.10
[156] S.V. Kuznetsov, V.A.Trofimov. GAS-PERMEABLE ZONES IN COAL SEAMS AND THE NATURE AND MECHANISM BY WHICH THEY FORM[J]. Journal of Mining Science. Vol. 35, No. 1, 1999: 19-25.
[157] Yu.F.Kovalenko, V.I.Karev. Dynamics of gas release from a coal seam in driving a working[J].Journal of Mining Science. Vol. 37, No. 1, 2001: 51-56.
[158] V. N. Odintsev. SUDDEN OUTBURST OF COAL AND GAS FAILURE OF NATURAL COAL AS A SOLUTION OF METHANE IN A SOLID SUBSTANCE[J]. Journal of Mining Science, Vol. 33, No. 6, 1997: 508-516.
[159] Zofia MAJEWSKA, Jerzy ZITEK. Acoustic emission and sorptive deformation induced in coals of various rank by the sorption-desorption of gas[J]. Acta Geophysica, vol.55, no.3, : 324-343
[160] V.N.Oparin, V.S.Ludzish, G.I.Kulakov,etc. Journal of Mining Science, Vol. 41, No. 2 , 2005: 93-104
[161] M.B. Wold, L.D. Connell, S.K. Choi. The role of spatial variability in coal seam parameters on gas outburst behaviour during coal mining[C]. International Journal of Coal Geology, Vol. 75, Issue 1, 13 June 2008, : 1-14
[2]李成武.煤与瓦斯突出危险状态灰色分类及预测技术研究[D].徐州:中国矿业大学出版社, 2005.4
[3]何学秋,王从银.从我国煤矿瓦斯灾害多发现象论安全科学基础研究的迫切性[J].中国科学基金,2002,5.
[4]吴中立.矿井通风与安全[M].徐州:中国矿业大学出版社,1988.
[5]邓聚龙.灰色控制系统[M].华中理工大学出版社,1997.10.
[6]刘思峰,郭天榜等.灰色系统理论及其应用[M].北京:科学出版社,1991.
[7]邓聚龙.灰色系统基本方法[M].武昌:华中工学院出版社,1987.
[8]邓聚龙.灰色系统理论教程[M].武昌:华中理工大学出版社,1989.
[9] [苏联]B.B.霍多特著,宋士钊,王佑安译.煤与瓦斯突出[M].北京:中国工业出版社,1966,5.
[10]马尚权,王恩元,何学秋等.煤与瓦斯突出预测预报方法[J].矿山机械,2001(5):71-72
[11]唐春安,徐小荷.岩石破裂过程失稳的尖点突变模型[J]岩石力学与工程学报,1990(2):100-107
[12]唐春安等.岩石破裂过程数值实验[M].北京科学出版社,2003
[13]章梦涛.冲击地压失稳理论与数值模拟计算[J].岩石力学与工程学报,1987(3):197-204
[14]章梦涛,徐曾和,潘一山.冲击地压和突出的统一失稳理论[J],煤炭学报,1991(4):48一53
[15]潘一山,章梦涛.冲击地压失稳理论的解析分析[J].岩石力学与工程学报,1996,Vol.15: 504-510
[16]徐曾和,章梦涛.冲击地压失稳理论及其应用[C].第二届全国岩石动力学学术会议论文集,宜昌,1990,10:332-339
[17]苗琦,杨胜强,欧晓英,陈祖云.煤与瓦斯突出灰色-神经网络预测模型的建立及应用[J].采矿与安全工程学报,2008,25,(3):309-312.
[18]虞和济.基于神经网络的智能诊断[M].北京:冶金工业出版社,2000.5
[19]戴葵.神经网络实现技术[M].长沙:国防科技大学出版社,1998
[20]由伟,刘亚秀,李永.用人工神经网络预测煤瓦斯突出[J].煤炭学报[J],1007,32,(3):385-387.
[21] John Grznar, Sameer Prasad, Jasmine Tata,.Neural networks and organizational systems: Modeling non-linear relationships[J], European Journal of Operational Research, Volume 181, Issue 2, 1 September 2007, 939-955.
[22] Manoj Khandelwal, T.N. Singh. Prediction of blast induced ground vibrations and frequency in opencast mine: A neural network approach[J]. Journal of Sound and Vibration, Volume 289, Issues 4-5, 7 February 2006, 711-725.
[23] K. Shanti Swarup, G. Sudhakar. Neural network approach to contingency screening and ranking in power systems[J], Neurocomputing, Volume 70, Issues 1-3, December 2006, 105-118.
[24] J.C. Cao, S.H. Cao. Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis[J]. Energy, Volume 31, Issue 15, December 2006, 3435-3445.
[25]聂百胜,何学秋,王恩元,刘贞堂等.煤与瓦斯突出预测技术研究现状及发展趋势[J].中国安全科学学报,Vol.13,2003(6).:40-43.
[26]H.F. Janas, E. Kunz. Evaluation of the danger of coal-gas outbursts based on gas pressure and permeability[J]. Fuel and Energy Abstracts Volume 37, Issue 3, May 1996,220.
[27]付建华,程远平.中国煤矿煤与瓦斯突出现状及防治对策[J].采矿与安全工程学报,2007.34(3): 253-259.
[28]吴财芳,曾勇.基于专家系统的煤与瓦斯突出预测知识库研究[J].煤田地质与勘探, 2004(2): 17-20.
[29]张小东,张子戌,秦勇.基于专家系统的煤与瓦斯突出区域预测研究[J].煤矿安全,2004(9): 1-3.
[30]蔡成功,张建国.煤与瓦靳突出规律的分析探讨[J].煤矿安全,2003(12):3-6.
[31]王英敏译.煤与瓦斯突出概论[M].北京:煤炭工业出版社.1958.5.
[32]中国矿业大学瓦斯组编.煤与瓦斯突出的防治[M].北京:煤炭工业出版社.1979.3
[33] A.T.艾鲁尼著.唐修义,宋德淑,王荣龙译.煤与瓦斯动力现象的预测与防治[M].北京:煤炭工业出版社.1992.9
[34]周世宁,林柏泉.煤层瓦斯赋存与流动理论[M].北京:煤炭工业出版社.1999.2:5-7.
[35]程伟.煤与瓦斯突出危险性预测与防治技术[M].徐州:中国矿业大学出版社,2003.7.
[36]何学秋.含瓦斯煤岩流变动力学[M].徐州:中国矿业大学出版社,1995.5.
[37]何学秋,刘明举.含瓦斯煤岩破坏电磁动力学[M].徐州:中国矿业大学出版社,1995.5.
[38]王恩元,何学秋,聂百胜,刘贞堂.电磁辐射法预测煤与瓦斯突出原理[J].中国矿业大学学报, Vol29,3,2000(5):225-229.
[39]撒占友,何学秋,王恩元.工作面煤与瓦斯突出电磁辐射的神经网络预测方法研究[J],煤炭学报,Vol29,5,2004(10):563-567.
[40]窦林名,何学秋,王恩元.冲击矿压预测的电磁辐射技术及应用,煤炭学报, Vol29,4, 2004(8):396-399.
[41]何学秋,王恩元,聂百胜等.煤岩流变电磁动力学[M].北京:煤炭工业出版社,2003.4.
[42]于不凡.国内外煤与瓦斯突出日常预测研究综述[M].煤炭科学总院重庆分院编.煤与瓦斯突出预测资料汇编,1987.
[43]中华人民共和国煤炭部.防治煤与瓦斯突出细则[M].北京:煤炭工业出版社,1995.
[44]王凯,俞启香.煤与瓦斯突出的非线性特征及预测模型[M].徐州:中国矿业大学出版社,2005,3
[45]工作面钻孔瓦斯涌出初速度特征分析[D].硕士学位论文.抚顺:煤炭科学研究总院抚顺分院,1985.
[46]王克全.突出煤层钻孔瓦斯涌出特点研究[D].硕士学位论文.重庆:煤炭科学研究总院重庆分院, 1986.
[47]胡千庭.WTC瓦斯突出仪及其应用[J].煤炭工程师,1994(6):2-6
[48]苏文叔.利用瓦斯涌出动态指标预测煤与瓦斯突出[J].煤炭工程师,1996(5):2-7
[49]南存全,冯夏庭.基于模式识别的煤与瓦斯突出区域预测[J].东北大学学报(自然科学版), 2004,25(9):903-906.
[50]周骏,曲云尧,周文涛,汤成.煤与瓦斯突出模式识别预测软件的设计原理[J].山东矿业学院学报,1996,15(1):61-66.
[51] Li S,Zhang H. Coal and gas outburst model recognition and regional prediction.In:Proceedings in Mining Science and Safety Technology.Beijing:Science Publishing House,2002.33
[52]张宏伟,李胜.煤与瓦斯突出危险性的模式识别和概率预测[J],岩石力学与工程学报, 2005,19(10):3577-3581.
[53]刘明举.计算机模式识别技术在煤与瓦斯突出预测中的应用[J].瓦斯地质,1989(12).
[54]王显政.煤矿安全新技术[M].北京:煤炭工业出版社,2002.
[55]魏建平.矿井动力灾害煤岩电磁辐射预警机理及其应用研究[D].徐州:中国矿业大学博士论文, 2004.
[56]李忠辉,王恩元,何学秋,刘贞堂,宋霞女.电磁辐射实时监测煤与瓦斯突出在煤矿的应用[J].煤炭科学技术,2005(9):31-33.
[57]王佑安,王魁军.工作面突出危险性预测敏感指标确定方法探讨[J],煤矿安全,1991(10).9-14
[58]杨光正,吴岷,张晓莉.模式识别[M].合肥:中国科学技术出版社,2001,7.
[59]吴逸飞译.模式识别-原理、方法及应用[M].北京:清华大学出版社,2002,11.
[60]边肇祺,张学工.模式识别[M].北京:清华大学出版社,2002,2
[61] C .J. Burges. A Tutorial on Support Vector Machines for Patern Recognition[J]. Data Mining and Knowledge Discovery.1998,2(2):121-16
[62]K. Balakrishnan, V. Honavar. Intelligent Diagnosis Systems. Improving Design of Intelligent Systems: Outstanding Problems and Some Methods for their Solution[J]. A Special Issue of the International Journal of Intelligent Systems. 1998, 8(3/4):239-290
[63] Schwutke U.M.,Veregge J.R.,etal. Cooperating Expert System for the Next Generation of Real Time Monitoring Application.IEEE 2th International Conferenceon Expert System for Develop. 1994: 210-214
[64] Hoskins J.C.,Kaliyu K.M. Fault Diagnosisin Complex Chemical Plants Using A rtificial Neural Network[J]. ArtificialIn telligence.1991,37(1):137-141
[65]何学秋.含瓦斯煤岩流变动力学[M].徐州:中国矿业大学出版社,1995.5
[66]蒋承林,俞启香.煤与瓦斯突出的球壳失稳机理及防治技术[M],徐州:中国矿业大学出版社,1998:114-116
[67]张铁岗.矿井瓦斯综合治理技术[M].北京:煤炭工业出版社,2001
[68]王凯.钻孔法研究预测煤与瓦斯突出的研究[D].博士论文.徐州:中国矿业大学出版社,1997
[69]吕绍林,何继善.瓦斯突出煤体的粒度分形研究[J].煤炭科学技术, 1999, 27(2): 46-48
[70]王凯,俞启香.非线性理论在煤与瓦斯突出研究中的应用[J].辽宁工程技术大学学报,1998,8(6):348-352
[71]王凯,俞启香.煤与瓦斯突出起动过程的突变理论研究[J].中国安全科学学报, 1998, 8(6):10-15.
[72]王轶波.煤巷掘进工作面瓦斯涌出的非线性特征及突出预测研究[D].硕士论文.徐州:中国矿业大学出版社,2004.
[73]李树刚.综放开采围岩活动及瓦斯运移[M].徐州:中国矿业大学出版社,2000.5.
[74] [苏联]O.H切尔诺夫,E.C罗赞采失著,宋士钊,于不凡译.瓦斯突出危险煤层井田的准备[M].北京:煤炭工业出版社,1980,8.
[75] [苏联]A.A.斯阔成基编,Γ.д.李金著,王英敏译.矿井瓦斯涌出量研究[M].北京:煤炭工业出版社,1958,5.
[76]于不凡,王佑安.煤矿瓦斯灾害防治及利用技术手册[M].北京:煤炭工业出版社,2000,8.
[77] Andrew R.Webb著,王萍,杨培龙,罗颖昕译.统计模式识别Statistical Pattern Recognition[M].北京:电子工业出版社,2004,10.
[78]Jain,A.K.,Duin, R.P.W.and Mao,J.(2000) Statistical pattern recognition:A revew[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,22(1):4-37
[79]孙即祥.现代模式识别[M].长沙:国防科技大学出版社,2002,1
[80]肖健华,吴今培.基于支持向量机的模式识别方法.五邑大学学报(自然科学版).第l6卷第1期,2002年3月:6-10
[81] Dominique M,Alistair B.Nonlinear blind source separation using kernels[J].IEEE Tram.On Neural Networks,2003,14(1):228—235.
[82] Mike Fugate,James R,Gattikers.Computer Intrusion Detection with Classification and Anomaly Detection Using SVMs[C].International Journal of Pattern Recognition and Artificial Intelligence,2003,17(3):441—458.
[83] P.T.桑德斯著.凌复华译.突变理论入门[M].上海;上海科学技术文献出版,1983.
[84]凌复华.突变理论及应用[M].上海:上海交通大学出版社,1985.
[85]王连国,缪协兴.基于尖点突变模型的矿柱失稳机理研究[J].采矿与安全工程学报, VoI.23 No.2, 2006(2):137-140.
[86]章梦涛,徐曾和,潘一山等.冲击地压和突出的统一失稳理论[J].煤炭学报1991.4:48—52
[87]郭文兵,邓喀中,邹友峰.条带煤粒的突变破坏换稳理论研究[J].中国矿业大学学报,2005,1: 77—80.
[89]马中飞,俞启香.煤与瓦斯承压散体失控突出机理的初步研究[J],煤炭学报2006.3:299—333.
[90]肖福坤,秦宪礼,张娟等.煤与瓦斯突出过程的突变分析[J],辽宁工程技术大学学报,2004.4: 442-444
[91]李玉,赵国景.煤层突出的突变模式[J],北京科技大学学报1995.1:5-9.
[92]勾攀峰,汪成兵,韦四洒.基于突变理论的深井巷道临界深度[J].岩石力学与工程学报,2004.24: 4237-4141.
[93]潘岳,张孝伍.狭窄煤柱岩爆的突变理论分析[J],岩石力学与工程学报2004.12:1797—1803.
[94]尹光志,李贺,鲜学福等.煤岩体失稳的理论模型[J],重庆大学学报1994.1:23—28.
[95]刘保县,鲜学福,刘新荣等.爆破激发煤瓦斯突出的研究[J],中国矿业2000.3:89—91
[96]王凯,俞启香.煤与瓦斯突出起动过程的突变理论研究[J],中国安全科学学报1998.6:10—15
[97]李晓伟,蒋承林,初始释放瓦斯膨胀能与煤体破碎程度的关系研究[J].煤矿安全,2008,5:1-3
[98]方健之,俞善炳,谈庆明.煤与瓦斯突出的层裂—粉碎模型[J],煤炭学报.1995.2:149-152.
[99]俞善炳.恒稳推进的煤与瓦斯突出[J],力学学报1988.2:97-106.
[100]俞善炳.煤与瓦斯突出的一维流动模型和启动判据[J]力学学报1992.4:418-431.
[101]周世宁.瓦斯在煤层中流动的机理[J].煤炭学报,V01.15 No.1, 1990(3):15-24.
[102]张广洋,谭学术,鲜学福.煤层瓦斯运移的数学模型[J].重庆大学学报,1994(4):53-57.
[103]科学技术文献出版社重庆分社[M].国外煤与瓦斯突出(第一集).1978.7:34-49.
[104]国家安全生产监督管理总局.低瓦斯煤矿也要安装瓦斯监测系统[J].煤炭企业管理,2006,4:9
[105]聂韧,赵旭生.掘进工作面瓦斯涌出动态指标预测突出危险性的探讨,矿业安全与环保,2004 (8):36-38.
[106]苏文叔.利用瓦斯涌出动态指标预测煤与瓦斯突出[J].煤炭工程师,1996(5):2-7.
[107]秦汝祥,张国枢,杨应迪.瓦斯涌出异常预报煤与瓦斯突出[J],煤炭学报,2006(10):599-602.
[108]李秋林.应用瓦斯涌出特征实施突出预报的初步探索[J],矿业安全与环保,2006(2):68-70.
[109]庄新田,庄新路,田莹.Hurst指数及股市的分形结构[J].东北大学学报(自然科学版),2003(9): 862-864
[110]张利平,王德智,夏军,牛存稳.R/S分析在洪水变化趋势预测中的应用研究[J].中国农村水利水电, 2005年第2期:38-40
[111] Hurst指数估算中存在的若干问题.地质科学,2004(2):177-182.
[112] F. LEPRETI, P. C. FANELLO, F. ZACCARO, V. CARBONE. Persistence of solar activity on small scales: Hurst analysis of time series coming from Hαflares[J]. Solar Physics, Volume 197, 2000(11) : 149-156.
[113] Peter Hall, Wolfgang H?rdle, Torsten Kleinow, Peter Schmidt.Semiparametric Bootstrap Approach to Hypothesis Tests and Confidence Intervals for the Hurst Coefficient[J]. Statistical Inference for Stochastic Processes, Volume 3, 2000(10):263-276.
[114] G. A. Sviridyuk, A. A. Zamyshlyaeva. The Phase Spaces of a Class of Linear Higher-Order SobolevType Equations[J]. DIFFERENTIAL EQUATIONS, Vol.42 No.2, 2006:296-278.
[115]刘树勇,朱石坚,俞翔.相空间重构优化研究,数据采集与处理:2008(1):65-68.
[116]林嘉宇,王跃科,黄芝平.语言信号相空间重构中的时间延迟的选择-复自相关法[J].信号处理, 1999,15(3):220-225.
[117]Liming W.,Robert Cawley.Smoothness implies determinism:A Method to Detect it in Time Series[J].Physical Review Letters,1994,73(8):1091-1094.
[118] Determination of Discrete Relaxation and Retardation Time Spectra from Dynamic Mechanical Data[J]. Rheologica Acta, Volume 28, Number 6, 1989(11):511-519.
[119] Jean-Charles, Robinet,Paul Sardini. The Effect of Rock Matrix Heterogeneities Near Fracture Walls on the Residence Time Distribution (RTD) of Solutes[J]. Transport in Porous Media, Volume 72, Number 3 , 2008(4):393-408.
[120] Mera M. ,Moran M. Degrees of freedom of a time Serirs [J]. Joural of Statistical Physics, 2002, Vol.106 (12):125-145.
[121]任志军,田心.高维混沌的研究现状与展望[J].国外医学生物学工程分册,2004,27(1):18-21.
[122] Wolf A. Determing Lyapunov Exponent from a Time Series[J].Physics, 1985,16D(3):285-317.
[123] Rosentein M.T. ,Collins J.J. ,Deluca C.J. A Practical method for Calculating Largest Lyapunov Exponents from Small Data Sets[J].Physical D, 1993,(65):117-134.
[124]陈士华,陆君安.混沌动力学初步.武汉:武汉水利电力大学出版社,1998.8.
[125]吕金虎,陆君安,陈士华.混沌时间序列分析及其应用.武汉:武汉水利电力大学出版社,2002.4.
[126]王俊国,周建中,付波等.基于混沌时间序列的电力负荷短期预测方法[J].水电能科学, 2006.4: 70-72.
[127]岳毅宏,韩文秀,王健.基于加权平均一阶发散度的混沌序列预测法[J].系数工程与电子技术, 2004.5: 40-42.
[128]林振山.长期预报的相空间理论和模式[M].北京:气象出版社,1993.3.
[129]陈淑燕,王炜.基于Lyapunov指数的交通量混沌预测方法[J].土木工程学报,2004.37(9):96-99.
[130]蒋传文,侯志俭,李涛,张勇传.基于小波分解的径流非线性预测[J].上海交通大学学报, 2002(7):1053-1056.
[131]殷光伟,郑丕谔.基于小波与混沌集成的中国股票市场预测[J].系统工程学报, Vo1.20, No.2,2005(4):180-184.
[132]任霞.小波分析在电力系数中的应用[M].北京:中国电力出版社,2003.11.
[133]孔即祥.现代模式识别[M].长沙:国防科技大学出版社,2002.1.
[134]杨凌霄,沈鹰,侯国栋,王成硕.基于支持向量机的煤与瓦斯突出预测研究[J].河南理工大学学报,2006(10):348-250.
[135]齐永锋,火元莲.支持向量机研究[J].甘肃联合大学学报(自然科学版),2005(4):23-26
[136]肖汉光,蔡从中.利用支持向量机SVM识别车辆类型[J].重庆大学学报(自然科学版), 2006(1):61-64.
[137] James K.Model selection in kernel machine classification with applications in bioinformatics[D]. New Mexico:The University of New Mexico Albuquerque,Doctor of Philosophy Statistics,2005.5.
[138] Nello Cristianini,John Shawe-Taylor著.李国正,王猛,曾华军译.An Introduction to Support Vector Machines and Other Kernel-based Learning Methods.支持向量机导论[M].北京:电子工业出版社,2004.3.
[139] Deloste D. ,Scholkopf B. Training Invariant Support Vector Machines[J]. Machine Learing, 2002, 46(1-2-3):161-190.
[140]吴涛.核函数的性质、方法信其在障碍检测中的应用[D].长沙:国防科技大学出版社博士论文,2003.
[141]李忠辉,王恩元,何学秋,刘贞堂,宋霞女.电磁辐射实时监测煤与瓦斯突出在煤矿的应用.煤炭科学技术,2005(9):31-33.
[142]国家发明专利(ZL98111185.8)和实用新型专利(ZL01272808.X).
[143]何学秋,聂百胜,王恩元等.矿井煤岩动力灾害电磁辐射预警技术[J].煤炭学报,32(1),2007. 1:56-59.
[144]潘爱民.计算机网络(第四版),北京:清华大学出版社,2004.
[145]范建华等.TCP/IP详解.卷1:协议[M],北京:机械工业出版社,2000.
[146]陆雪莹.TCP/IP详解.卷2:实现[m],北京:机械工业出版社,2004.
[147]胡谷雨等.TCP/IP详解.卷3:TCP事务协议、HTTP、NNTP和UNIX域协议[M]北京:机械工业出版社,2000.
[148] H.M.Deitel,P.L.Deitel.著.刘文红译.Visual C#2005大学教程(第二版).北京:电子工业出版社, 2007.7.
[149]蒋承林.煤与瓦斯突出阵面推进过程及力学条件分析[J].中国矿业太学学报,1994.23(4): 1～9
[150]蒋承林.煤与瓦斯突出过程中能量耗散规律的研究[J].煤炭学报,1996,21(2):l73～178
[151]蒋承林.煤样的韧始释放瓦斯膨胀能测定[J].岩石力学与工程学报,1996,15(4):39～400
[152]蒋承林.石门揭穿含瓦斯煤层时动力现象的球壳失稳机理研究[D],[博上学位论文].徐州:中国矿业大学采矿工程系,1994
[153]蒋承林俞启香.煤与瓦斯突出机理的球壳失稳假说[J].煤矿安全,1995,2:16-25
[154]吴方同.数学物理方程[M].武汉:武汉大学出版社,2001.11
[155]赵振海.数学物理方程与特殊函数学习指导与习题全解[M].大连,大连理工大学出版社,2003.10
[156] S.V. Kuznetsov, V.A.Trofimov. GAS-PERMEABLE ZONES IN COAL SEAMS AND THE NATURE AND MECHANISM BY WHICH THEY FORM[J]. Journal of Mining Science. Vol. 35, No. 1, 1999: 19-25.
[157] Yu.F.Kovalenko, V.I.Karev. Dynamics of gas release from a coal seam in driving a working[J].Journal of Mining Science. Vol. 37, No. 1, 2001: 51-56.
[158] V. N. Odintsev. SUDDEN OUTBURST OF COAL AND GAS FAILURE OF NATURAL COAL AS A SOLUTION OF METHANE IN A SOLID SUBSTANCE[J]. Journal of Mining Science, Vol. 33, No. 6, 1997: 508-516.
[159] Zofia MAJEWSKA, Jerzy ZITEK. Acoustic emission and sorptive deformation induced in coals of various rank by the sorption-desorption of gas[J]. Acta Geophysica, vol.55, no.3, : 324-343
[160] V.N.Oparin, V.S.Ludzish, G.I.Kulakov,etc. Journal of Mining Science, Vol. 41, No. 2 , 2005: 93-104
[161] M.B. Wold, L.D. Connell, S.K. Choi. The role of spatial variability in coal seam parameters on gas outburst behaviour during coal mining[C]. International Journal of Coal Geology, Vol. 75, Issue 1, 13 June 2008, : 1-14