多传感器信息融合理论及在矿井瓦斯突出预警系统中的应用研究
|
摘要
煤矿瓦斯灾害是我国主要灾害之一,矿井瓦斯突出预测是一个十分复杂的理论和实验技术课题,原因在于瓦斯突出是一个包含地质学、物理、化学等多学科、多种效应交叉的复杂现象,并且对于这一灾害缺少有效的监测方法和手段。论文针对瓦斯突出这一不确定性和非线性灾害问题,建立了多传感器信息融合瓦斯突出预测系统模型,并分别对融合系统的数据层、特征层和决策层进行了分析和研究,构建了一个基于多规则决策的瓦斯突出智能预警系统,从而有效地提高对瓦斯突出预测的准确度。
论文通过对多传感器信息融合一般框架结构分析,提出了基于多传感器信息融合的瓦斯突出预警系统结构,以及特征级和决策级分层融合模型,并通过多传感器管理子系统实现反馈建立预测系统闭环控制模型。
在分析目前已有的突出指标及临界值确定依据的基础上,引入层次分析法得到几个典型突出指标的权重排序,对瓦斯突出多传感器融合预测系统的数据来源信息进行分析。并根据选取的重要影响指标选择瓦斯突出监测传感器,由于静态传感器定点布置存在一定的局限性,提出了利用主动嗅觉技术研究瓦斯监测动态传感器,以增加多传感器预测系统的实时、动态数据源。
分析多传感器信息融合各层次算法的优缺点,选取人工神经网络作为特征层分层融合算法。通过瓦斯突出实例,对比分析了三种改进的BP神经网络模型,实验数据表明三种改进的BP神经网络能有效解决传统BP网络收敛速度慢和易陷入局部极优的缺陷,从而有效改善融合的效果和速度。基于神经网络固有的缺点,进一步提出用Dempster-Shafter (D-S)证据理论作为决策级融合方法,构成特征级和决策级的分层融合结构,增加决策的可靠性。
D-S证据理论以其优越的不确定性推理成为很好的决策融合算法,论文对决策层D-S证据理论进行了深入研究,总结了证据理论存在的主要问题,并提出了对应的解决办法。特别针对D-S证据理论中证据冲突的问题,引入证据距离、证据源可信度等概念,提出了一种新的合成规则。该规则把冲突信息按证据源的可信度进行分配,对一致性证据采用反映聚焦程度的与运算。理论分析和数值实验表明本文提出的合成规则对高冲突性证据和一致性证据都非常有效,能够解决多传感器信息来源之间的证据冲突问题。
为了定量表示实际应用中的模糊概念和数据,论文研究了D-S证据理论推广到模糊集理论的方法。总结了目前比较典型的证据理论向模糊集推广的方法和结论。针对前人的不足,给出了一种新的模糊集合之间相似度的定义,并进一步提出一种有效组合模糊证据理论的决策层融合算法,并给出其数学证明。通过实例验证了该算法能更有效地获取模糊焦元的变化信息,具有更好的融合效果。
论文选取五个典型高瓦斯矿区突出数据,验证了本文提出的多传感器信息融合瓦斯突出预测系统模型的可行性和有效性;并根据改进的D-S证据理论融合规则,采用Windows Vista sp1 + Visual Studio 2008 sp1系统开发平台实现了矿井瓦斯突出智能预警系统,为现场工程技术人员提供实时决策辅助支持。经过矿区现场数据验证该瓦斯突出多规则决策融合系统能够提供准确、可靠的灾害预测,有效地提高了煤矿安全管理水平。
Gas outbursts in mines are among the main disasters in China. Gas outburst prediction is a complex theoretical and technical project, as the phenomenon of gas outbursts results from multiple effects of a geological, physical and chemical nature, and there is a lack of effective monitoring methods for this disaster. For resolving the seeminlgy unpredictable and non-linear problem of gas outbursts, a new model of a gas outburst prediction system based on multi-sensor information fusion is put forward, and the data level, feature level and decision level is analysed and studied respectively. And an intelligent decision prediction system is presented, the example results of this system show that it could significantly increase the accuracy of gas outburst prediction.
After summarizing the general architecture of multi-sensor information fusion, this work puts forward a new model of a gas outburst prediction system. It is based on multi-sensor information fusion, which is described as hierarchical fusion in the feature fusion level and the decision level. And it is a closed contrl model through the feedback of sensor management system.
Based on analysing the current indexes and their critical values which were put forward by different researchers, this thesis presents a hierarchical analysis method for arranging the main typical influencing factors in the order of their significance and chooses the most suitable sensors according to the most important factors. After analysing the limitations of local static sensors, a real-time dynamic sensor is studied using the active olfaction technology.
As the merits and demerits of each multi-sensor information fusion method, the Artificial Neural Network (ANN) is chosen as feature fusion algorithm. Three improved BP neural network could improve the learning velocity and capability of convergence of traditional BP neural network, which is validated by example data analysis of gas outbursts. As the inherent disadvantages of ANN, the Dempster-Shafter (D-S) evidence theory is presented for decision fusion, which could increase the credibility of the decision-making.
As one of the more effective uncertain reasoning methods among the information fusion methods, the D-S evidence theory has proven to be a good decision fusion algorithm. This work sums up the main deficiencies of the D-S evidence theory, and gives the corresponding solutions. It puts forward new combination rules of the D-S Evidence Theory for resolving the problem of evidence conflicts based on introducing the distance between the evidence, the credibility of evidence sources, and other notions, and also provides the mathematical proofs for these rules. The improved rules allocate the conflicts to various focal elements according to the credibility of the coherence evidence. The AND-algorithm is adopted to combine the coherence evidence, which reflects the intersection of focus elements. The corresponding mathematical and theoretical analysis proves that the given rules are rational and effective for both highly conflicting and coherent evidence, and that they can deal with conflicting information from different sensors.
For combing the D-S evidence theory and the fuzzy set theory, typical current methods and conclusions of extending the evidence theory to fuzzy sets are summarized. Pointing out the disadvantages of previous methods, this paper introduces a new definition of the similarity degree between fuzzy sets, and also a decision-making fusion algorithm which can combine fuzzy evidence effectively.
Mathematical proofs and example analyses validate the new algorithm and demonstrate that it is more effective, acquires more information on the change of fuzzy focal elements, and produces better fusion results than existing methods. In order to validate the model of the gas outburst prediction system based on multi-sensor information presented in this thesis, the gas outburst data of five typical mines are chosen. According to the improved combination rules of the D-S evidence theory, an intelligent prediction system of gas outbursts is developed on a Windows Vista +Visual Studio 2008 software platform which provides real-time decision support for underground workers in coal mines. The results from the analysis of our gas outburst multi-regular decision fusion system show that the system supplies accurate and credible disaster prediction, which effectively improves the level of coal mine safety management.
论文通过对多传感器信息融合一般框架结构分析,提出了基于多传感器信息融合的瓦斯突出预警系统结构,以及特征级和决策级分层融合模型,并通过多传感器管理子系统实现反馈建立预测系统闭环控制模型。
在分析目前已有的突出指标及临界值确定依据的基础上,引入层次分析法得到几个典型突出指标的权重排序,对瓦斯突出多传感器融合预测系统的数据来源信息进行分析。并根据选取的重要影响指标选择瓦斯突出监测传感器,由于静态传感器定点布置存在一定的局限性,提出了利用主动嗅觉技术研究瓦斯监测动态传感器,以增加多传感器预测系统的实时、动态数据源。
分析多传感器信息融合各层次算法的优缺点,选取人工神经网络作为特征层分层融合算法。通过瓦斯突出实例,对比分析了三种改进的BP神经网络模型,实验数据表明三种改进的BP神经网络能有效解决传统BP网络收敛速度慢和易陷入局部极优的缺陷,从而有效改善融合的效果和速度。基于神经网络固有的缺点,进一步提出用Dempster-Shafter (D-S)证据理论作为决策级融合方法,构成特征级和决策级的分层融合结构,增加决策的可靠性。
D-S证据理论以其优越的不确定性推理成为很好的决策融合算法,论文对决策层D-S证据理论进行了深入研究,总结了证据理论存在的主要问题,并提出了对应的解决办法。特别针对D-S证据理论中证据冲突的问题,引入证据距离、证据源可信度等概念,提出了一种新的合成规则。该规则把冲突信息按证据源的可信度进行分配,对一致性证据采用反映聚焦程度的与运算。理论分析和数值实验表明本文提出的合成规则对高冲突性证据和一致性证据都非常有效,能够解决多传感器信息来源之间的证据冲突问题。
为了定量表示实际应用中的模糊概念和数据,论文研究了D-S证据理论推广到模糊集理论的方法。总结了目前比较典型的证据理论向模糊集推广的方法和结论。针对前人的不足,给出了一种新的模糊集合之间相似度的定义,并进一步提出一种有效组合模糊证据理论的决策层融合算法,并给出其数学证明。通过实例验证了该算法能更有效地获取模糊焦元的变化信息,具有更好的融合效果。
论文选取五个典型高瓦斯矿区突出数据,验证了本文提出的多传感器信息融合瓦斯突出预测系统模型的可行性和有效性;并根据改进的D-S证据理论融合规则,采用Windows Vista sp1 + Visual Studio 2008 sp1系统开发平台实现了矿井瓦斯突出智能预警系统,为现场工程技术人员提供实时决策辅助支持。经过矿区现场数据验证该瓦斯突出多规则决策融合系统能够提供准确、可靠的灾害预测,有效地提高了煤矿安全管理水平。
Gas outbursts in mines are among the main disasters in China. Gas outburst prediction is a complex theoretical and technical project, as the phenomenon of gas outbursts results from multiple effects of a geological, physical and chemical nature, and there is a lack of effective monitoring methods for this disaster. For resolving the seeminlgy unpredictable and non-linear problem of gas outbursts, a new model of a gas outburst prediction system based on multi-sensor information fusion is put forward, and the data level, feature level and decision level is analysed and studied respectively. And an intelligent decision prediction system is presented, the example results of this system show that it could significantly increase the accuracy of gas outburst prediction.
After summarizing the general architecture of multi-sensor information fusion, this work puts forward a new model of a gas outburst prediction system. It is based on multi-sensor information fusion, which is described as hierarchical fusion in the feature fusion level and the decision level. And it is a closed contrl model through the feedback of sensor management system.
Based on analysing the current indexes and their critical values which were put forward by different researchers, this thesis presents a hierarchical analysis method for arranging the main typical influencing factors in the order of their significance and chooses the most suitable sensors according to the most important factors. After analysing the limitations of local static sensors, a real-time dynamic sensor is studied using the active olfaction technology.
As the merits and demerits of each multi-sensor information fusion method, the Artificial Neural Network (ANN) is chosen as feature fusion algorithm. Three improved BP neural network could improve the learning velocity and capability of convergence of traditional BP neural network, which is validated by example data analysis of gas outbursts. As the inherent disadvantages of ANN, the Dempster-Shafter (D-S) evidence theory is presented for decision fusion, which could increase the credibility of the decision-making.
As one of the more effective uncertain reasoning methods among the information fusion methods, the D-S evidence theory has proven to be a good decision fusion algorithm. This work sums up the main deficiencies of the D-S evidence theory, and gives the corresponding solutions. It puts forward new combination rules of the D-S Evidence Theory for resolving the problem of evidence conflicts based on introducing the distance between the evidence, the credibility of evidence sources, and other notions, and also provides the mathematical proofs for these rules. The improved rules allocate the conflicts to various focal elements according to the credibility of the coherence evidence. The AND-algorithm is adopted to combine the coherence evidence, which reflects the intersection of focus elements. The corresponding mathematical and theoretical analysis proves that the given rules are rational and effective for both highly conflicting and coherent evidence, and that they can deal with conflicting information from different sensors.
For combing the D-S evidence theory and the fuzzy set theory, typical current methods and conclusions of extending the evidence theory to fuzzy sets are summarized. Pointing out the disadvantages of previous methods, this paper introduces a new definition of the similarity degree between fuzzy sets, and also a decision-making fusion algorithm which can combine fuzzy evidence effectively.
Mathematical proofs and example analyses validate the new algorithm and demonstrate that it is more effective, acquires more information on the change of fuzzy focal elements, and produces better fusion results than existing methods. In order to validate the model of the gas outburst prediction system based on multi-sensor information presented in this thesis, the gas outburst data of five typical mines are chosen. According to the improved combination rules of the D-S evidence theory, an intelligent prediction system of gas outbursts is developed on a Windows Vista +Visual Studio 2008 software platform which provides real-time decision support for underground workers in coal mines. The results from the analysis of our gas outburst multi-regular decision fusion system show that the system supplies accurate and credible disaster prediction, which effectively improves the level of coal mine safety management.
引文
[1]俞启香.矿井瓦斯防治[M].徐州:中国矿业大学出版社,1992.
[2]程远平,俞启香.中国煤矿区域性瓦斯治理技术的发展[J].采矿与安全工程学报,2007,04:383-390.
[3]俞启香,程远平,蒋承林,周世宁.高瓦斯特厚煤层煤与卸压瓦斯共采原理及实践[J].中国矿业大学学报.2004,02:127-131.
[4]王永先,曹焕举,孙晓震.用多元线性回归分析法预测矿井深部瓦斯涌出量[J].焦作工学院学报(自然科学版),2000,04: 246-249.
[5]唐春安,芮勇勤,刘红元等.含瓦斯“试样”突出现象的RFPA数值模拟[J].煤炭学报,2005,05: 501-504.
[6]郝吉生,袁崇孚.模糊神经网络技术在煤与瓦斯突出预测中的应用[J].煤炭学报.1999,06:15-17.
[7]中华人民共和国煤炭工业部制定.防治煤与瓦斯突出细则[M].北京:煤炭工业出版社,1995.
[8]于不凡主编.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.
[9]牛国庆,颜爱华,刘明举,煤与瓦斯突出过程中温度变化的实验研究[J],西安科技学院学报,2003,23(3):28-31.
[10] M.J. Liu, X.Q He. Electromagnetic response of outburst-prone coal [J]. International Journal of coal Geologe, 2001,45:155-162.
[11]撒占友,何学秋,张永亮,王恩元.煤岩电磁辐射效应及其在煤与瓦斯突出预测中的应用[J].中国矿业大学学报.2006,01: 48-51.
[12]周丕昌,刘万伦,李伟.大河边向斜地质构造对煤与瓦斯突出的影响[J].采矿与安全工程学报.2009,01: 55-59.
[13]刘金海,冯涛,谢东海,刘辉.煤与瓦斯突出预测的距离判别分析方法[J].煤田地质与勘探.2009,01 :26-27.
[14]吕绍林,何继善,李舟波.非接触式瓦斯突出预测方法[J].物探与化探,2000,24(1): 4-6.
[15]刘章平.地震检波器串并组合性能分析[J].江汉石油科技.2007,03: 16-18.
[16]刘金海,冯涛,谢东海,刘辉.煤与瓦斯突出预测的距离判别分析方法[J].煤田地质与勘探.2009,01: 26-32.
[17]漆旺生,凌标灿,蔡嗣经.煤与瓦斯突出预测研究动态及展望[J].中国安全科学学报,2003,13(12): 20-24 .
[18]聂韧,赵阳生.掘进工作面瓦斯涌出动态指标预测突出危险性的探讨[J].矿业安全与环保, 2004: 31(4): 36-39.
[19] L.David Hall, L.James(编),杨露菁,耿伯英(译).多传感器数据融合手册[M].北京:电子工业出版社,2008.5.
[20] J.Guan, Y.He, Y.Peng. Distributed CFAR Detection Based on Local Test Statistic [J]. Signal Proceeding, 2000,80: 373-379.
[21] M. K. Hamiltion, T. A. Kipp. ATR architecture for multisensor fusion [C]. SPIE, 1996, 275(5):126-133.
[22]李圣怡,吴学忠,范大鹏.多传感器融合理论及在智能制造系统中的应用[M].长沙:国防科技大学出版社,1998.
[23] N. Li; J. Li; Z.H Zhang; Y.J. Fang; Bo Xi. Fault Diagnosis of Rotating System based on Multi-sensor Data Fusion [C]. The Sixth World Congress on Intelligent Control and Automation, WCICA 2006. 2006,2: 5466– 5470.
[24] Y.T. Zang; K.Yuan; W. Zou, H.S Hu. A Practical Exploration Approach Applied to Robot Simultaneous Localization and Map Building Process [C]. IEEE International Conference on Industrial Technology, ICIT2006, 2006:144-149.
[25] Y.Chen, Zhang, H.Chang. X.Y. Huang, A Novel Vehicle Safety Traffic Pre-warning Decision on Multi-sensor Information Fusion [C]. International Conference on Convergence Information Technology, 2007:318– 322.
[26] D.k.Allen. Intergrating vision and touch for object recognition tasks [J]. Internation Journal of Robot. 1988, 7(6):15-33.
[27]张宏伟.岩体应力状态研究与矿井动力现象区域预测[D].辽宁工程技术大学:博士论文. 1999.
[28]聂百胜,何学秋,王恩元等.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, 2003,13(6): 40-43.
[29]何友,王国宏等.多传感器信息融合及应用[M].北京:电子工业出版社, 2000,11.
[30] H.Varma, K.Fadaie, M.Habbane, and J.Stochausen. Confusion in data fusion [J]. International Journal of Remote Sensing, 2003, 24(4):627-636.
[31]徐耀松,付华,王丹丹.数据融合技术在综掘工作面瓦斯预测中的应用[J].矿业快报,2004, 416(2): 21-23.
[32] J.Malcolm. Beynon. Lianne Jones. Catherine A. Holt. Classification of Osteoarthritic and Normal Knee Function Using Three-Dimensional Motion Analysis and the Dempster-Shafer Theory of Evidence [J]. IEEE Transactions on Systems, Man, And Cybernetics-Part A: Systems and Humans. 2006, 36(1): 173-186.
[33]吴强.基于神经网络的煤与瓦斯突出预测模型[J].中国安全科学学报. 2001, 11(4): 69-72.
[34]肖红飞,何学秋,刘黎明.改进BP算法在煤与瓦斯突出预测中的应用[J].中国安全科学学报. 2003, 13(9): 59-61.
[35]张春,题正义,周云鹏.神经网络计算方法在煤与瓦斯突出预测中的应用[J].煤炭工程. 2004(7): 49-52.
[36] J.B Yang. J. Liu. J. Wang etc. Belief Rule-Base Inference Methodology Using the Evidential Reasoning Approach-RIMER [R]. IEEE Trans on Sys, Man, and Cyber-Part A: Systems and Humans. 2006, 36(2): 266-285.
[37]朱厚任,朱卫东,杨善林.基于证据理论的证券组合优化分析[J].合肥工业大学学报(自然科学版), 2005,28(10): 1296-1298.
[38]林志贵,徐立中,黄凤辰等.基于D-S理论的多源水质检测数据融合处理[J].计算机工程与应用, 2004,10: 3-5.
[39]李水友,刘智勇.基于D-S证据理论的区域交通自适应协调控制[J].控制理论与应用, 2005,22(1):157-160.
[40]吕文红,吴祈宗,邵全.证据理论在新产品开发方案可行性评估中的应用[J].运筹与管理,2004,13(5):111-115.
[41]林柏泉,崔恒信.矿井瓦斯防治理论与技术[M].徐州:中国矿业大学出版社, 1998.
[42]秦汝祥.煤与瓦斯突出预报研究现状综述[J].能源技术与管理,2005,1(3):6-8.
[43]李胜.煤与瓦斯突出区域预测的模式识别方法研究[D].辽宁工程技术大学, 2004.
[44]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].重庆大学, 2004.
[45]蔡峰.煤巷掘进过程中煤与瓦斯突出机理的研究[D].淮南安徽理工大学,2005.
[46]石必明,俞启香,王凯.远程保护层开采上覆煤层透气性动态演化规律试验研究[J].岩石力学与工程学报.2006,09:1917-1921.
[47]李成武.煤与瓦斯突出危险状态灰色分类方法及预测技术研究[D].徐州:中国矿业大学, 2005.
[48]赵焕臣,许树柏,金生.层次分析法——一种简易的新决策方法[M].北京:科学出版社,1986.
[49]范金志,郭德勇,张建国.层次分析法确定煤与瓦斯突出影响因素的权重[J].矿业安全与环保, 2004, 31(3): 4-8.
[50] W. Li, J.A.Farrell, S.Pang, and R.M Arrieta. Moth-Inspired Chemical Plume Tracking on an Autonomous Underwater Vehicle [J]. IEEE Transactions on Robotics, 2006, 22(2):292-301.
[51] W. Li, J.W Zhang. Moth-Inspired Chemical Plume Tracing by Integration of Fuzzy Following-Obstacle Behavior[C]. WICC2008, Hongkong,2008,6.
[52]孟庆浩,李飞.主动嗅觉研究现状[J].机器人, 2006, 28(1): 89-96.
[53]李俊彩,孟庆浩,梁琼.基于进化梯度搜索的机器人主动嗅觉仿真研究[J].机器人, 2007, 29(3): 234-238.
[54] A. Lilienthal, T. DuckettA. stereo electronic nose for a mobile inspection robot [A]. ROSE’03. 1st InternationalWorkshop on Robotic Sensing [C]. USA: IEEE, 2003:1-6.
[55] H.Ishida, T.Nakamoto, T.Moriizumi. Study ofodor compass [C]. IEEE/SICE/RSJ InternationalConference onMultisensorFusion and Integration for Intelligent Systems. Washington, DC, USA: IEEE, 1996: 222-226.
[56]权太范.信息融合.神经网络-模糊推理理论与应用[M].北京:国防工业出版社,2002.
[57]王耀南,李树涛.多传感器信息融合及其应用综述[J].控制与决策, 2001, 16(5): 518-522.
[58]刘云生,彭建平.信息融合的研究.计算机工程与应用[J],2005,3: 181-183.
[59]何友,关欣,王国宏.多传感器信息融合研究进展与展望[J] .宇航学报, 2005, 26(4): 524-530.
[60] V.Belur.Dasarathy. Sensor fusion potential exploitation—innovative architecutes and inustractive applications [C]. Proceedings of the IEEE, 1997, 85(1):24-38.
[61]刘同明,夏祖勋,解洪成.数据融合技术及其应用[M].北京:国防工业出版社,1998.9.
[62] Durrant-Whyte, H.F., B.Y.S. Rao, and H.Hu. Towards a fully decentralized architecture for multi-sensor data fusion in Robotics and Automation [C]. Proceedings of IEEE International Conference on Automation and Robotics. 1990 ,2: 87-90.
[63]刘先省.传感器管理方法研究[D].西北工业大学, 2000.
[64] M.E. Liggins, et al., Distributed Fusion Architectures and Algorithms for Target Tracking [C]. Proceedings of the IEEE Conference on Automation. 1997. 85(1): 95-107.
[65]赵宗贵等编译.多传感器数据融合[M].北京:机械电子工业部第二十八研究所,1993,2.
[66]康伟.多传感器多目标跟踪数据融合技术研究[D].西安西北工业大学图书馆,1999.
[67] K.P.Zavoleas, and M.M. Kokar. Algebraic specifications of sensor data fusion systems in Intelligent Control[C]. Proceedings of the IEEE International Symposium. 1993(1):23-28.
[68] D. Noble. Distributed Situation Assessment [C]. Proceedings of International Conference on Multisource-Multisensor Information Fusion, Las Vegas, Nevada, USA, 1998:478-485.
[69]徐耀松,付华,王丹丹.采用数据融合技术在综掘工作面瓦斯预测中的应用[J].矿业快报,2004, 2(416): 21-23.
[70]毛土艺.多传感器目标状态的融合估计[D],北京航空航天大学博士学位论文,2001.
[71]梁继民.多传感器决策融合方法研究[D].西安电子科技大学博士学位论文,2001.
[72]张菊秀.多传感器信息融合技术和发展[J].电子世界,2005(4):6-7.
[73]刘同明,夏祖勋,解洪成.数据融合技术及其应用[M].北京:国防工业出版社,1998.
[74]潘泉,于昕,程咏梅,张洪才.信息融合理论的基本方法与进展[J].自动化学报,2003, 29(4): 599-615.
[75]王慧斌.基于主体的企业信息融合系统关键技术研究[D].中国矿业大学, 2003.
[76]刘先省,传感器管理方法研究[D].西北工业大学,2000.
[77] S.W. Wang, Jin-Bo Wang. Automatic sensor evaluation in BMS commissioning of building refrigeration systems [J]. Automation in Construction, 2002, (11): 59-73.
[78] L. Y. Pao ,N. T. Baltz. Control of Sensor Information in Distributed Multisensor Systems [C]. American Control Conference, 1998:2397-2401.
[79] G. Carl. Schaefer, Jr. Kenneth ,J. Hintz. Sensor Management in a Sensor Rich Environment [J/OL]. www.elsevier.com.
[80] A .Gregory. McIntyre, J.Kenneth Hintz.. A Comprehensive Approach to Sensor Management [J/OL], Part II: A New Hierarchical Model. www.elsevier.com.
[81] R. P. S. Mahler. Global Posterior densities for Sensor Management, Acquisition, Tracking, and Pointing XII [J]. The International Society for Optical Engineering, 1998,3365: 252-263.
[82] R. P.S.Mahler. Global Optimal Sensor Allocation. Proceedings of the Ninth National Symposium on Sensor Fusion[C]. Naval Postgraduate School, Nonterey CA, 1996,1: 347-366.
[83] S. Musick, K. Kastella, R. Mahler. A Practical Implementation of Joint, Multitarget Probabilities [J]. The International Society for Optical Engineering, 1998,2232: 26-37.
[84]刘先省,潘泉,张玉法,张洪才.基于效能函数的多传感器管理算法研究[J].控制与决策, 1999,14: 581-584.
[85]杨万海.多传感器信息融合及其应用[M] .西安:西安科技大学出版社,2000.
[86]康耀华.信息融合理论与应用[M] .西安:西安科技大学出版社,2002.
[87] A .Lawrence.Klein.(著),戴亚平,刘征,郁光辉(译).多传感器信息融合理论及应用(第二版)[M] .北京:北京理工大学出版社,2004.
[88]杨波.基于小波的像素级图像融合算法研究[D].上海交通大学,2008.
[89] M.A Abidi and R.C Gonzalez., Data Fusion in Robotics and Machine Intelligence[R], Academic Press, 1992.
[90]边平勇,李金静.非线性贝叶斯动态模型及其随机模拟算法[J].统计与决策, 2007, 13(241):133-134.
[91]赵明瞻,赵文成.广义卡尔曼滤波递推公式的证明[J].沈阳航空工业学院学报, 2006, 23(3): 82-84.
[92]李志强,陆晓兵.小样本统计决策理论—统计学习理论[J].统计教育,2005,8: 8-10.
[93] Smarandache. Unification of fusion theories (UFT) [J]. International Journal of AppliedMathematics & Statistics, 2004, 02: 1-14.
[94] F. Smarandache. A unifying field in logics: neutrosophic logic, neutrosophy, neutrosophic set, probability and statistics [M]. USA:Amercian Research Press,Rehoboth, 2000: 1-144.
[95]段新生.证据理论与决策、人工智能[M] .北京:中国人民大学出版社,1993.
[96] J.Y.Fan An Approach to Fauct Dignosis of the chemical processes via Neural Network[J]. Journal of AICHE,1993,39(1): 33-37.
[97]李亚飞.基于粗糙集的证据理论方法研究[D].合肥工业大学, 2004.
[98]王伟.人工神经网络—入门与应用[M].北京:北京航空航天大学出版社,1995.
[99]蔺杰.数据融合的神经计算[D].浙江大学博士学位论文,2005.
[100]戈志华,李如翔,宋之平等.基于BP神经网络的旋转机械故障诊断[J].华东电力大学学报,1998,22(6):32-35.
[101]杜华英,赵跃龙.人工神经网络典型模型的比较研究[J].计算机技术与发展,2006,05: 33-35.
[102]白连平,陈秀真.提高BP网络学习速度的自适应算法[J].系统仿真学报,2001(S1):30-32.
[103]申东日,冯少,陈义俊. BP网络改进方法概述[J].化工自动化及仪表,2000,27(1):30-32.
[104]李晓峰,刘光中.人工神经网络BP算法的改进及其应用[J] .四川大学学报,2000,32(2): 105-109.
[105]杜国明,陈晓翔,曾志芳.快速二阶BP神经网络在公路交通量预测中的应用[J].中南公路工程,2006,02:13-15.
[106]马永强,霍振宇,杨珠.一种增加动量项的改进BP算法实现[J].科技情报开发与经济,2006,08: 18-21.
[107]张晶,张权,王欣.一种新的基于统计向量和神经网络的边缘检测方法[J].计算机研究与发展,2006,05:32-35.
[108]张瑞军,张志清. BP神经网络的网络拓扑可重构及实现方法[J].武汉科技大学学报(自然科学版),2003,01:16-19.
[109]郑德忠,葛文谦.基于数值优化的改进BP算法在旋转机械故障诊断中的应用[J].传感技术学报,2005,03:3-17.
[110]易正俊.多源信息智能融合算法[D].重庆大学博士学位论文,2002.
[111] R. Yager. On the Dempster-Shafer framework and new combination rules[J]. Information Sciences, 1987, 41: 93-137.
[112]孙全,叶秀清,顾伟康.一种新的基于证据理论的合成公式[J].电子学报,2000, 28(8): 117-119.
[113]刘洪永,程远平,周红星,王海锋,俞启香.立井煤与瓦斯突出危险性分析及防突措施[J].采矿与安全工程学报.2007,02:173-177.
[114]罗志增,蒋静坪.相关证据的融合及其在机器人多感觉信息融合中的应用[J].传感技术学报,2000,3: 177-182.
[115]马国清,赵亮,李鹏.基于Dempster-Shafer证据推理的多传感器信息融合技术及应用[J].现代电子技术, 2003, 19: 41-44.
[116]肖志宏,罗志增,叶明.用证据理论实现多信息融合的一种改进算法[J].机器人. 2000, 22(1): 7-11.
[117] B. Tessem. Approximations for efficient computation in the theory of evidence[J]. Artificial Intelligence, 1993, 61(2): 315-329.
[118]杨善林,朱卫东,任明仑.基于可变参数优化的相关证据合成方法研究[J].管理科学学报,2003, 6(5): 12-16.
[119] T. Denoeux. An evidence-theoretic neural network classifier[C]. IEEE Int. Conf. on Syst. Man, and Cybern., Vancouver, 1995, 25(3): 712-714.
[120]李剑峰,乐光新,尚勇.基于改进型D-S证据理论的决策层融合滤波算法[J].电子学报, 2004, 32(7): 1160-1164.
[121]王刚.证据理论及其在图像目标识别中的应用[D].西北工业大学硕士论文, 2002.
[122]林志贵.基于证据理论的信息融合研究及其在水质监测中的应用[D].南京:河海大学, 2005.
[123]吴根秀.模糊证据理论[J].计算机与现代化, 1998, 54(2): 1-4.
[124]黄鹍,陈森发,周振国,张文红.基于粗集理论和证据理论的多源信息融合方法[J].信息与控制,2004, 33(4): 422-425.
[125]李亚飞.基于粗糙集的证据理论方法研究[D].合肥,合肥工业大学, 2004.
[126]邓勇,施文康.一种改进的证据推理组合规则[J].上海交通大学学报, 2003, 37(8): 1275-1278.
[127]李弼程,王波,魏俊等.一种有效的证据理论合成公式[J].数据采集与处理,2002, 17(1): 33-36.
[128]杜峰,施文康,邓勇.证据特征提取及其在证据理论改进中的应用[J].上海交通大学学报,2004,38: 164-168.
[129]肖明珠,陈光禹.一种改进的证据合成公式[J].数据采集与处理,2004, 19(4): 467-469.
[130] A. Jousselme, D Grenier, E Bosse. A new distance between two bodies of evidence [J]. Information Fusion, 2002, 3: 237-239.
[131] Y. Z. Miao, H. X. Zhang, J. W. Zhang, X. P. Ma. Improvement of the Combination Rules of the D-S Evidence Theory Based on Dealing with the Evidence Conflict[C]. Proceedings of the 2008 IEEE International Conference on Information and Automation, 2008:331-336.
[132] Y.Z.Miao, X. P. Ma, H.X. Zhang, J.W. Zhang, Z. X. Zhao. An Improved Extension of the D-S Evidence Theory to Fuzzy sets[C]. Proceedings of the 3th International Multi-Conference on Computing in the Global Information Technology, Athens, Greece, 2008:148-153.
[133] M.Ishizuka, KS Fu, JTP Yao. Inference procedures under uncertainty for the problem-reduction method[J]. Information Science, 1982, 28(3): 179-206.
[134] H. Ogawa, K S Fu. An inexact inference for damage assessment of existing structures[J]. International Journal of Man-Machine Studies, 1985, 22: 295-306.
[135] R.Yager. Generalized probabilities of fuzzy events from fuzzy belief structures[J]. Information Science, 1982, 28: 45-62.
[136] M.S. Yang, T C Chen, Wu K L. Generalized belief function, plausibility function, and Dempster’s combinational rule to fuzzy sets[J]. International Journal of Intelligent Systems, 2003, 18: 925-937.
[137] R. Haenni. Are alternatives to Dempster’s rule of combination real alternatives? Comments on About the belief function combination and the conflict management problem[J]. Information Fusion, 2002, 3(4): 237-239.
[138] J.Yen. A Dempster-Shafer Approach to Diagnosing Hierarchical Hypotheses[J]. Communications of the ACM, 1989, 32(5): 573-585.
[139] R.Kruse et al. Uncertainty and Vagueness in Knowledge Based Systems (numerical methods) [R]. New York: Springer-verlag, 1991,1:328-330.
[140]刘大有,钟少春,高雅卿.具有两级不确定性的推理模型[J].软件学报, 1993, 4(3): 45-52.
[141]刘大有,李岳峰.二级不确定性的内涵处理方法[J].软件学报, 1997, 8(7): 535-540.
[142]吴根秀.模糊证据理论[J].计算机与现代化, 1998, 54(2): 1-4.
[143]陈水利,李敬功,王向公.模糊集理论及其应用[M].北京:科学出版社, 2005.
[144] J. Yen. Generalizing the D-S theory to fuzzy set[J]. IEEE Transactions on System, Man, and Cybernetics, 1990, 20(3): 559-570.
[2]程远平,俞启香.中国煤矿区域性瓦斯治理技术的发展[J].采矿与安全工程学报,2007,04:383-390.
[3]俞启香,程远平,蒋承林,周世宁.高瓦斯特厚煤层煤与卸压瓦斯共采原理及实践[J].中国矿业大学学报.2004,02:127-131.
[4]王永先,曹焕举,孙晓震.用多元线性回归分析法预测矿井深部瓦斯涌出量[J].焦作工学院学报(自然科学版),2000,04: 246-249.
[5]唐春安,芮勇勤,刘红元等.含瓦斯“试样”突出现象的RFPA数值模拟[J].煤炭学报,2005,05: 501-504.
[6]郝吉生,袁崇孚.模糊神经网络技术在煤与瓦斯突出预测中的应用[J].煤炭学报.1999,06:15-17.
[7]中华人民共和国煤炭工业部制定.防治煤与瓦斯突出细则[M].北京:煤炭工业出版社,1995.
[8]于不凡主编.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.
[9]牛国庆,颜爱华,刘明举,煤与瓦斯突出过程中温度变化的实验研究[J],西安科技学院学报,2003,23(3):28-31.
[10] M.J. Liu, X.Q He. Electromagnetic response of outburst-prone coal [J]. International Journal of coal Geologe, 2001,45:155-162.
[11]撒占友,何学秋,张永亮,王恩元.煤岩电磁辐射效应及其在煤与瓦斯突出预测中的应用[J].中国矿业大学学报.2006,01: 48-51.
[12]周丕昌,刘万伦,李伟.大河边向斜地质构造对煤与瓦斯突出的影响[J].采矿与安全工程学报.2009,01: 55-59.
[13]刘金海,冯涛,谢东海,刘辉.煤与瓦斯突出预测的距离判别分析方法[J].煤田地质与勘探.2009,01 :26-27.
[14]吕绍林,何继善,李舟波.非接触式瓦斯突出预测方法[J].物探与化探,2000,24(1): 4-6.
[15]刘章平.地震检波器串并组合性能分析[J].江汉石油科技.2007,03: 16-18.
[16]刘金海,冯涛,谢东海,刘辉.煤与瓦斯突出预测的距离判别分析方法[J].煤田地质与勘探.2009,01: 26-32.
[17]漆旺生,凌标灿,蔡嗣经.煤与瓦斯突出预测研究动态及展望[J].中国安全科学学报,2003,13(12): 20-24 .
[18]聂韧,赵阳生.掘进工作面瓦斯涌出动态指标预测突出危险性的探讨[J].矿业安全与环保, 2004: 31(4): 36-39.
[19] L.David Hall, L.James(编),杨露菁,耿伯英(译).多传感器数据融合手册[M].北京:电子工业出版社,2008.5.
[20] J.Guan, Y.He, Y.Peng. Distributed CFAR Detection Based on Local Test Statistic [J]. Signal Proceeding, 2000,80: 373-379.
[21] M. K. Hamiltion, T. A. Kipp. ATR architecture for multisensor fusion [C]. SPIE, 1996, 275(5):126-133.
[22]李圣怡,吴学忠,范大鹏.多传感器融合理论及在智能制造系统中的应用[M].长沙:国防科技大学出版社,1998.
[23] N. Li; J. Li; Z.H Zhang; Y.J. Fang; Bo Xi. Fault Diagnosis of Rotating System based on Multi-sensor Data Fusion [C]. The Sixth World Congress on Intelligent Control and Automation, WCICA 2006. 2006,2: 5466– 5470.
[24] Y.T. Zang; K.Yuan; W. Zou, H.S Hu. A Practical Exploration Approach Applied to Robot Simultaneous Localization and Map Building Process [C]. IEEE International Conference on Industrial Technology, ICIT2006, 2006:144-149.
[25] Y.Chen, Zhang, H.Chang. X.Y. Huang, A Novel Vehicle Safety Traffic Pre-warning Decision on Multi-sensor Information Fusion [C]. International Conference on Convergence Information Technology, 2007:318– 322.
[26] D.k.Allen. Intergrating vision and touch for object recognition tasks [J]. Internation Journal of Robot. 1988, 7(6):15-33.
[27]张宏伟.岩体应力状态研究与矿井动力现象区域预测[D].辽宁工程技术大学:博士论文. 1999.
[28]聂百胜,何学秋,王恩元等.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, 2003,13(6): 40-43.
[29]何友,王国宏等.多传感器信息融合及应用[M].北京:电子工业出版社, 2000,11.
[30] H.Varma, K.Fadaie, M.Habbane, and J.Stochausen. Confusion in data fusion [J]. International Journal of Remote Sensing, 2003, 24(4):627-636.
[31]徐耀松,付华,王丹丹.数据融合技术在综掘工作面瓦斯预测中的应用[J].矿业快报,2004, 416(2): 21-23.
[32] J.Malcolm. Beynon. Lianne Jones. Catherine A. Holt. Classification of Osteoarthritic and Normal Knee Function Using Three-Dimensional Motion Analysis and the Dempster-Shafer Theory of Evidence [J]. IEEE Transactions on Systems, Man, And Cybernetics-Part A: Systems and Humans. 2006, 36(1): 173-186.
[33]吴强.基于神经网络的煤与瓦斯突出预测模型[J].中国安全科学学报. 2001, 11(4): 69-72.
[34]肖红飞,何学秋,刘黎明.改进BP算法在煤与瓦斯突出预测中的应用[J].中国安全科学学报. 2003, 13(9): 59-61.
[35]张春,题正义,周云鹏.神经网络计算方法在煤与瓦斯突出预测中的应用[J].煤炭工程. 2004(7): 49-52.
[36] J.B Yang. J. Liu. J. Wang etc. Belief Rule-Base Inference Methodology Using the Evidential Reasoning Approach-RIMER [R]. IEEE Trans on Sys, Man, and Cyber-Part A: Systems and Humans. 2006, 36(2): 266-285.
[37]朱厚任,朱卫东,杨善林.基于证据理论的证券组合优化分析[J].合肥工业大学学报(自然科学版), 2005,28(10): 1296-1298.
[38]林志贵,徐立中,黄凤辰等.基于D-S理论的多源水质检测数据融合处理[J].计算机工程与应用, 2004,10: 3-5.
[39]李水友,刘智勇.基于D-S证据理论的区域交通自适应协调控制[J].控制理论与应用, 2005,22(1):157-160.
[40]吕文红,吴祈宗,邵全.证据理论在新产品开发方案可行性评估中的应用[J].运筹与管理,2004,13(5):111-115.
[41]林柏泉,崔恒信.矿井瓦斯防治理论与技术[M].徐州:中国矿业大学出版社, 1998.
[42]秦汝祥.煤与瓦斯突出预报研究现状综述[J].能源技术与管理,2005,1(3):6-8.
[43]李胜.煤与瓦斯突出区域预测的模式识别方法研究[D].辽宁工程技术大学, 2004.
[44]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].重庆大学, 2004.
[45]蔡峰.煤巷掘进过程中煤与瓦斯突出机理的研究[D].淮南安徽理工大学,2005.
[46]石必明,俞启香,王凯.远程保护层开采上覆煤层透气性动态演化规律试验研究[J].岩石力学与工程学报.2006,09:1917-1921.
[47]李成武.煤与瓦斯突出危险状态灰色分类方法及预测技术研究[D].徐州:中国矿业大学, 2005.
[48]赵焕臣,许树柏,金生.层次分析法——一种简易的新决策方法[M].北京:科学出版社,1986.
[49]范金志,郭德勇,张建国.层次分析法确定煤与瓦斯突出影响因素的权重[J].矿业安全与环保, 2004, 31(3): 4-8.
[50] W. Li, J.A.Farrell, S.Pang, and R.M Arrieta. Moth-Inspired Chemical Plume Tracking on an Autonomous Underwater Vehicle [J]. IEEE Transactions on Robotics, 2006, 22(2):292-301.
[51] W. Li, J.W Zhang. Moth-Inspired Chemical Plume Tracing by Integration of Fuzzy Following-Obstacle Behavior[C]. WICC2008, Hongkong,2008,6.
[52]孟庆浩,李飞.主动嗅觉研究现状[J].机器人, 2006, 28(1): 89-96.
[53]李俊彩,孟庆浩,梁琼.基于进化梯度搜索的机器人主动嗅觉仿真研究[J].机器人, 2007, 29(3): 234-238.
[54] A. Lilienthal, T. DuckettA. stereo electronic nose for a mobile inspection robot [A]. ROSE’03. 1st InternationalWorkshop on Robotic Sensing [C]. USA: IEEE, 2003:1-6.
[55] H.Ishida, T.Nakamoto, T.Moriizumi. Study ofodor compass [C]. IEEE/SICE/RSJ InternationalConference onMultisensorFusion and Integration for Intelligent Systems. Washington, DC, USA: IEEE, 1996: 222-226.
[56]权太范.信息融合.神经网络-模糊推理理论与应用[M].北京:国防工业出版社,2002.
[57]王耀南,李树涛.多传感器信息融合及其应用综述[J].控制与决策, 2001, 16(5): 518-522.
[58]刘云生,彭建平.信息融合的研究.计算机工程与应用[J],2005,3: 181-183.
[59]何友,关欣,王国宏.多传感器信息融合研究进展与展望[J] .宇航学报, 2005, 26(4): 524-530.
[60] V.Belur.Dasarathy. Sensor fusion potential exploitation—innovative architecutes and inustractive applications [C]. Proceedings of the IEEE, 1997, 85(1):24-38.
[61]刘同明,夏祖勋,解洪成.数据融合技术及其应用[M].北京:国防工业出版社,1998.9.
[62] Durrant-Whyte, H.F., B.Y.S. Rao, and H.Hu. Towards a fully decentralized architecture for multi-sensor data fusion in Robotics and Automation [C]. Proceedings of IEEE International Conference on Automation and Robotics. 1990 ,2: 87-90.
[63]刘先省.传感器管理方法研究[D].西北工业大学, 2000.
[64] M.E. Liggins, et al., Distributed Fusion Architectures and Algorithms for Target Tracking [C]. Proceedings of the IEEE Conference on Automation. 1997. 85(1): 95-107.
[65]赵宗贵等编译.多传感器数据融合[M].北京:机械电子工业部第二十八研究所,1993,2.
[66]康伟.多传感器多目标跟踪数据融合技术研究[D].西安西北工业大学图书馆,1999.
[67] K.P.Zavoleas, and M.M. Kokar. Algebraic specifications of sensor data fusion systems in Intelligent Control[C]. Proceedings of the IEEE International Symposium. 1993(1):23-28.
[68] D. Noble. Distributed Situation Assessment [C]. Proceedings of International Conference on Multisource-Multisensor Information Fusion, Las Vegas, Nevada, USA, 1998:478-485.
[69]徐耀松,付华,王丹丹.采用数据融合技术在综掘工作面瓦斯预测中的应用[J].矿业快报,2004, 2(416): 21-23.
[70]毛土艺.多传感器目标状态的融合估计[D],北京航空航天大学博士学位论文,2001.
[71]梁继民.多传感器决策融合方法研究[D].西安电子科技大学博士学位论文,2001.
[72]张菊秀.多传感器信息融合技术和发展[J].电子世界,2005(4):6-7.
[73]刘同明,夏祖勋,解洪成.数据融合技术及其应用[M].北京:国防工业出版社,1998.
[74]潘泉,于昕,程咏梅,张洪才.信息融合理论的基本方法与进展[J].自动化学报,2003, 29(4): 599-615.
[75]王慧斌.基于主体的企业信息融合系统关键技术研究[D].中国矿业大学, 2003.
[76]刘先省,传感器管理方法研究[D].西北工业大学,2000.
[77] S.W. Wang, Jin-Bo Wang. Automatic sensor evaluation in BMS commissioning of building refrigeration systems [J]. Automation in Construction, 2002, (11): 59-73.
[78] L. Y. Pao ,N. T. Baltz. Control of Sensor Information in Distributed Multisensor Systems [C]. American Control Conference, 1998:2397-2401.
[79] G. Carl. Schaefer, Jr. Kenneth ,J. Hintz. Sensor Management in a Sensor Rich Environment [J/OL]. www.elsevier.com.
[80] A .Gregory. McIntyre, J.Kenneth Hintz.. A Comprehensive Approach to Sensor Management [J/OL], Part II: A New Hierarchical Model. www.elsevier.com.
[81] R. P. S. Mahler. Global Posterior densities for Sensor Management, Acquisition, Tracking, and Pointing XII [J]. The International Society for Optical Engineering, 1998,3365: 252-263.
[82] R. P.S.Mahler. Global Optimal Sensor Allocation. Proceedings of the Ninth National Symposium on Sensor Fusion[C]. Naval Postgraduate School, Nonterey CA, 1996,1: 347-366.
[83] S. Musick, K. Kastella, R. Mahler. A Practical Implementation of Joint, Multitarget Probabilities [J]. The International Society for Optical Engineering, 1998,2232: 26-37.
[84]刘先省,潘泉,张玉法,张洪才.基于效能函数的多传感器管理算法研究[J].控制与决策, 1999,14: 581-584.
[85]杨万海.多传感器信息融合及其应用[M] .西安:西安科技大学出版社,2000.
[86]康耀华.信息融合理论与应用[M] .西安:西安科技大学出版社,2002.
[87] A .Lawrence.Klein.(著),戴亚平,刘征,郁光辉(译).多传感器信息融合理论及应用(第二版)[M] .北京:北京理工大学出版社,2004.
[88]杨波.基于小波的像素级图像融合算法研究[D].上海交通大学,2008.
[89] M.A Abidi and R.C Gonzalez., Data Fusion in Robotics and Machine Intelligence[R], Academic Press, 1992.
[90]边平勇,李金静.非线性贝叶斯动态模型及其随机模拟算法[J].统计与决策, 2007, 13(241):133-134.
[91]赵明瞻,赵文成.广义卡尔曼滤波递推公式的证明[J].沈阳航空工业学院学报, 2006, 23(3): 82-84.
[92]李志强,陆晓兵.小样本统计决策理论—统计学习理论[J].统计教育,2005,8: 8-10.
[93] Smarandache. Unification of fusion theories (UFT) [J]. International Journal of AppliedMathematics & Statistics, 2004, 02: 1-14.
[94] F. Smarandache. A unifying field in logics: neutrosophic logic, neutrosophy, neutrosophic set, probability and statistics [M]. USA:Amercian Research Press,Rehoboth, 2000: 1-144.
[95]段新生.证据理论与决策、人工智能[M] .北京:中国人民大学出版社,1993.
[96] J.Y.Fan An Approach to Fauct Dignosis of the chemical processes via Neural Network[J]. Journal of AICHE,1993,39(1): 33-37.
[97]李亚飞.基于粗糙集的证据理论方法研究[D].合肥工业大学, 2004.
[98]王伟.人工神经网络—入门与应用[M].北京:北京航空航天大学出版社,1995.
[99]蔺杰.数据融合的神经计算[D].浙江大学博士学位论文,2005.
[100]戈志华,李如翔,宋之平等.基于BP神经网络的旋转机械故障诊断[J].华东电力大学学报,1998,22(6):32-35.
[101]杜华英,赵跃龙.人工神经网络典型模型的比较研究[J].计算机技术与发展,2006,05: 33-35.
[102]白连平,陈秀真.提高BP网络学习速度的自适应算法[J].系统仿真学报,2001(S1):30-32.
[103]申东日,冯少,陈义俊. BP网络改进方法概述[J].化工自动化及仪表,2000,27(1):30-32.
[104]李晓峰,刘光中.人工神经网络BP算法的改进及其应用[J] .四川大学学报,2000,32(2): 105-109.
[105]杜国明,陈晓翔,曾志芳.快速二阶BP神经网络在公路交通量预测中的应用[J].中南公路工程,2006,02:13-15.
[106]马永强,霍振宇,杨珠.一种增加动量项的改进BP算法实现[J].科技情报开发与经济,2006,08: 18-21.
[107]张晶,张权,王欣.一种新的基于统计向量和神经网络的边缘检测方法[J].计算机研究与发展,2006,05:32-35.
[108]张瑞军,张志清. BP神经网络的网络拓扑可重构及实现方法[J].武汉科技大学学报(自然科学版),2003,01:16-19.
[109]郑德忠,葛文谦.基于数值优化的改进BP算法在旋转机械故障诊断中的应用[J].传感技术学报,2005,03:3-17.
[110]易正俊.多源信息智能融合算法[D].重庆大学博士学位论文,2002.
[111] R. Yager. On the Dempster-Shafer framework and new combination rules[J]. Information Sciences, 1987, 41: 93-137.
[112]孙全,叶秀清,顾伟康.一种新的基于证据理论的合成公式[J].电子学报,2000, 28(8): 117-119.
[113]刘洪永,程远平,周红星,王海锋,俞启香.立井煤与瓦斯突出危险性分析及防突措施[J].采矿与安全工程学报.2007,02:173-177.
[114]罗志增,蒋静坪.相关证据的融合及其在机器人多感觉信息融合中的应用[J].传感技术学报,2000,3: 177-182.
[115]马国清,赵亮,李鹏.基于Dempster-Shafer证据推理的多传感器信息融合技术及应用[J].现代电子技术, 2003, 19: 41-44.
[116]肖志宏,罗志增,叶明.用证据理论实现多信息融合的一种改进算法[J].机器人. 2000, 22(1): 7-11.
[117] B. Tessem. Approximations for efficient computation in the theory of evidence[J]. Artificial Intelligence, 1993, 61(2): 315-329.
[118]杨善林,朱卫东,任明仑.基于可变参数优化的相关证据合成方法研究[J].管理科学学报,2003, 6(5): 12-16.
[119] T. Denoeux. An evidence-theoretic neural network classifier[C]. IEEE Int. Conf. on Syst. Man, and Cybern., Vancouver, 1995, 25(3): 712-714.
[120]李剑峰,乐光新,尚勇.基于改进型D-S证据理论的决策层融合滤波算法[J].电子学报, 2004, 32(7): 1160-1164.
[121]王刚.证据理论及其在图像目标识别中的应用[D].西北工业大学硕士论文, 2002.
[122]林志贵.基于证据理论的信息融合研究及其在水质监测中的应用[D].南京:河海大学, 2005.
[123]吴根秀.模糊证据理论[J].计算机与现代化, 1998, 54(2): 1-4.
[124]黄鹍,陈森发,周振国,张文红.基于粗集理论和证据理论的多源信息融合方法[J].信息与控制,2004, 33(4): 422-425.
[125]李亚飞.基于粗糙集的证据理论方法研究[D].合肥,合肥工业大学, 2004.
[126]邓勇,施文康.一种改进的证据推理组合规则[J].上海交通大学学报, 2003, 37(8): 1275-1278.
[127]李弼程,王波,魏俊等.一种有效的证据理论合成公式[J].数据采集与处理,2002, 17(1): 33-36.
[128]杜峰,施文康,邓勇.证据特征提取及其在证据理论改进中的应用[J].上海交通大学学报,2004,38: 164-168.
[129]肖明珠,陈光禹.一种改进的证据合成公式[J].数据采集与处理,2004, 19(4): 467-469.
[130] A. Jousselme, D Grenier, E Bosse. A new distance between two bodies of evidence [J]. Information Fusion, 2002, 3: 237-239.
[131] Y. Z. Miao, H. X. Zhang, J. W. Zhang, X. P. Ma. Improvement of the Combination Rules of the D-S Evidence Theory Based on Dealing with the Evidence Conflict[C]. Proceedings of the 2008 IEEE International Conference on Information and Automation, 2008:331-336.
[132] Y.Z.Miao, X. P. Ma, H.X. Zhang, J.W. Zhang, Z. X. Zhao. An Improved Extension of the D-S Evidence Theory to Fuzzy sets[C]. Proceedings of the 3th International Multi-Conference on Computing in the Global Information Technology, Athens, Greece, 2008:148-153.
[133] M.Ishizuka, KS Fu, JTP Yao. Inference procedures under uncertainty for the problem-reduction method[J]. Information Science, 1982, 28(3): 179-206.
[134] H. Ogawa, K S Fu. An inexact inference for damage assessment of existing structures[J]. International Journal of Man-Machine Studies, 1985, 22: 295-306.
[135] R.Yager. Generalized probabilities of fuzzy events from fuzzy belief structures[J]. Information Science, 1982, 28: 45-62.
[136] M.S. Yang, T C Chen, Wu K L. Generalized belief function, plausibility function, and Dempster’s combinational rule to fuzzy sets[J]. International Journal of Intelligent Systems, 2003, 18: 925-937.
[137] R. Haenni. Are alternatives to Dempster’s rule of combination real alternatives? Comments on About the belief function combination and the conflict management problem[J]. Information Fusion, 2002, 3(4): 237-239.
[138] J.Yen. A Dempster-Shafer Approach to Diagnosing Hierarchical Hypotheses[J]. Communications of the ACM, 1989, 32(5): 573-585.
[139] R.Kruse et al. Uncertainty and Vagueness in Knowledge Based Systems (numerical methods) [R]. New York: Springer-verlag, 1991,1:328-330.
[140]刘大有,钟少春,高雅卿.具有两级不确定性的推理模型[J].软件学报, 1993, 4(3): 45-52.
[141]刘大有,李岳峰.二级不确定性的内涵处理方法[J].软件学报, 1997, 8(7): 535-540.
[142]吴根秀.模糊证据理论[J].计算机与现代化, 1998, 54(2): 1-4.
[143]陈水利,李敬功,王向公.模糊集理论及其应用[M].北京:科学出版社, 2005.
[144] J. Yen. Generalizing the D-S theory to fuzzy set[J]. IEEE Transactions on System, Man, and Cybernetics, 1990, 20(3): 559-570.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |