港口企业生产安全评价体系及预警研究
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摘要
党的十六大确定了全面建设小康社会的奋斗目标,强调“高度重视安全生产,确保国家财产和人民生命安全”;2005年2月28日,国家安全生产监督管理局升格为国家安全生产监督管理总局,这说明严峻的安全生产形势暴露出安全生产管理中存在的问题得到了中央领导的高度重视。因此研究安全生产管理问题,具有重要的现实意义。
我国加入WTO后,作为连接水陆经济、贸易、文化的重要交会点-港口,越来越显现出它对区域经济及社会发展的重要促动作用。入世之后,进出口贸易的迅猛增长给港口企业提供了充足的货源,但同时也给港口企业提出了严峻的考验-在现有港口管理模式下,能否即保证港口吞吐量的稳步攀升,又保障港口生产的安全运行。总之,入世使中国港口企业面临着前所未有的机遇与挑战。
本文根据一定的安全科学方法和步骤以及相应的安全法规、条例、标准等,在充分调查研究的基础上,分析港口企业生产过程中各方面的安全因素,对生产的安全状况进行了分析,从而为制定安全评价体系和预警研究提供了科学依据。
本文综合利用安全系统工程、计算机软件工程等理论和先进技术,对安全评价和预警进行了深入地、系统的研究,从而建立了基于支持向量机的港口企业生产安全预警模型。论文的主要内容如下:
1 在研究我国港口企业安全生产管理的现状和存在的问题的同时,介绍了世界发达国家安全生产管理的经验,分析在我国当前形势下,构建港口企业生产安全评价与预警的必要性和迫切性。
2 从体系的建立和系统结构的量化等方面对安全评价框架-安全评价准则体系的建立进行了研究。
3 对支持向量机理论进行基本概念上的介绍,并深入探讨了SVM算法的基本理论。
4 研究了支持向量机在安全预警工作中的工作机理,并将支持向量机应用于安全预警中。
5 总结了本文的研究工作,对今后的研究进行了展望。
The Communist Party the 16th National Congress Convention has set the new objective of the struggle of overall building up well-off society for the whole nation. It is stressed that "highlighting safety production to ensure the security of national property and people's life". On the Feb. 28th, 2005, the level of National Safety Production Surveillance Administrative Bureau was upgraded as General Bureau of National Safety Production and Surveillance Administration. Such change can be interpreted that the leadership from National Central Communist Party paid a great attention to the existing safety production problems exposed out against the background of rigid safety production situation. As a result, it is realistically significant to study the safety production administration problems in China.After China's entry into WTO, port as a intersection point connecting land and water economy, trade, and culture, plays a more and more important role than ever before for promoting area economy and social development. After access to WTO, rapid increase of import and export trade has brought sufficient cargo sources to the port business, but at the same time, made port companies face the new ordeal,-----i.e. by the mode of modern port management, whether it is possible to havestable increase of port business throughput without the loss of safety security performance at the port. In general, being a formal WTO membership is presenting to Chinese port enterprises unprecedented opportunities and challenges.In this paper, according to certain safety scientific approaches, steps, relevant codes, standards, criterion, etc., through ample research and study, analyzed every kinds of safety factors and studied the safety status in the production process of port enterprise. Thus it can provide scientific evidence for making safety assessment and producing Safety Early-Warning Model.By synthetically using safety system engineering, computer software engineering theories and advanced technologies, made a profound and systemic research into safety assessment and early-warning working mechanism in this paper, and then established the Safety Early-Warning Model for port enterprise production based on support vector machine. The following are main study contents:1 Through analyzed the present situation of safety production and safety management of Chinese port enterprises and introduced experience and safety management of the developed countries, pointed out that it is great necessary and exigent to establish a new system of safety management imminently for the port production.2 Researched on the framework of safety assessment —Safety Assessment Criteria System in the term of the system foundation and system structural quantifying, etc.3 The basic concept of the support vector machine theory has been introduced
and the basic theoretical properties have been deeply discussed.4 This paper studied Support Vector Machine's (SVM) working mechanism in safety early warning, and applied it to safety early warning.5 Last section draws a conclusion from the whole research and makes an outlook for future works.
我国加入WTO后,作为连接水陆经济、贸易、文化的重要交会点-港口,越来越显现出它对区域经济及社会发展的重要促动作用。入世之后,进出口贸易的迅猛增长给港口企业提供了充足的货源,但同时也给港口企业提出了严峻的考验-在现有港口管理模式下,能否即保证港口吞吐量的稳步攀升,又保障港口生产的安全运行。总之,入世使中国港口企业面临着前所未有的机遇与挑战。
本文根据一定的安全科学方法和步骤以及相应的安全法规、条例、标准等,在充分调查研究的基础上,分析港口企业生产过程中各方面的安全因素,对生产的安全状况进行了分析,从而为制定安全评价体系和预警研究提供了科学依据。
本文综合利用安全系统工程、计算机软件工程等理论和先进技术,对安全评价和预警进行了深入地、系统的研究,从而建立了基于支持向量机的港口企业生产安全预警模型。论文的主要内容如下:
1 在研究我国港口企业安全生产管理的现状和存在的问题的同时,介绍了世界发达国家安全生产管理的经验,分析在我国当前形势下,构建港口企业生产安全评价与预警的必要性和迫切性。
2 从体系的建立和系统结构的量化等方面对安全评价框架-安全评价准则体系的建立进行了研究。
3 对支持向量机理论进行基本概念上的介绍,并深入探讨了SVM算法的基本理论。
4 研究了支持向量机在安全预警工作中的工作机理,并将支持向量机应用于安全预警中。
5 总结了本文的研究工作,对今后的研究进行了展望。
The Communist Party the 16th National Congress Convention has set the new objective of the struggle of overall building up well-off society for the whole nation. It is stressed that "highlighting safety production to ensure the security of national property and people's life". On the Feb. 28th, 2005, the level of National Safety Production Surveillance Administrative Bureau was upgraded as General Bureau of National Safety Production and Surveillance Administration. Such change can be interpreted that the leadership from National Central Communist Party paid a great attention to the existing safety production problems exposed out against the background of rigid safety production situation. As a result, it is realistically significant to study the safety production administration problems in China.After China's entry into WTO, port as a intersection point connecting land and water economy, trade, and culture, plays a more and more important role than ever before for promoting area economy and social development. After access to WTO, rapid increase of import and export trade has brought sufficient cargo sources to the port business, but at the same time, made port companies face the new ordeal,-----i.e. by the mode of modern port management, whether it is possible to havestable increase of port business throughput without the loss of safety security performance at the port. In general, being a formal WTO membership is presenting to Chinese port enterprises unprecedented opportunities and challenges.In this paper, according to certain safety scientific approaches, steps, relevant codes, standards, criterion, etc., through ample research and study, analyzed every kinds of safety factors and studied the safety status in the production process of port enterprise. Thus it can provide scientific evidence for making safety assessment and producing Safety Early-Warning Model.By synthetically using safety system engineering, computer software engineering theories and advanced technologies, made a profound and systemic research into safety assessment and early-warning working mechanism in this paper, and then established the Safety Early-Warning Model for port enterprise production based on support vector machine. The following are main study contents:1 Through analyzed the present situation of safety production and safety management of Chinese port enterprises and introduced experience and safety management of the developed countries, pointed out that it is great necessary and exigent to establish a new system of safety management imminently for the port production.2 Researched on the framework of safety assessment —Safety Assessment Criteria System in the term of the system foundation and system structural quantifying, etc.3 The basic concept of the support vector machine theory has been introduced
and the basic theoretical properties have been deeply discussed.4 This paper studied Support Vector Machine's (SVM) working mechanism in safety early warning, and applied it to safety early warning.5 Last section draws a conclusion from the whole research and makes an outlook for future works.
引文
[1] 安全生产、劳动保护政策法规系列专辑编委会编著.安全生产法专辑[M].北京:中国劳动出版社,2002
[2] 俞来宝.中国加入WTO后港口面临的机遇和挑战[J].中国港口,2005,18(11):20～21.
[3] 刘婷,魏国兴.港口安全生产特性分析[J].中国水运,2000,18(4):32～33.
[4] 余廉.企业预警管理论[M].河北:河北科学技术出版社,1999
[5] 陈宝智,王金波.安全管理[M].天津:天津大学出版社,1999
[6] 谢庆森,王秉权.安全人机工程[M].天津:天津大学出版社,1999
[7] 汪元辉.安全系统工程[M].天津:天津大学出版社,1999
[8] 谢进光.对当前港口安全管理工作的几点思考[J].中国港口,2001,12(8):12～13.
[9] 丁国良.交通运输企业经营管理[M].北京:人民交通出版社,1999
[10] 吴永富,方芳.港口生产管理[M].武汉:华中理工大学出版社,1989
[11] 宋德驰等.港口装卸工艺[M].北京:人民交通出版社,1994
[12] 施欣.水运企业现代管理理论方法与应用[M].北京:人民交通出版社,2000
[13] 杨步明,候世安,周通学等.劳动安全法规选编[R].山东:日照港务局安全生产委员会,1997
[14] 国家安全生产检查管理局、国家煤矿安全监察局.国家安全生产科技发展规划(交通运输建筑领域研究报告)(2004～2010)[R].2003.
[15] Everett J G, Thompson W S. Experience Modification Rating For Workers' Compensation Insurance[J]. Journal of Construction Engineering and Management, 2005, 121(1): 66～78.
[16] 顾培亮.系统分析[M].北京:机械工业出版社,1990.
[17] 顾培亮.系统分析与协调[M].天津:天津大学出版社,1998.
[18] 李哲,袁治平.建立递阶层次结构模型的方法探讨[J].系统工程理论方法应用,1995,4(2):71～73.
[19] 许绛垣,罗云,樊运晓.系统安全分析评价技术方法对比研究[J].劳动保护科学技术,1998,18(4):24～25.
[20] 陈晓剑,梁粱.系统评价方法及应用[M].北京:中国科学技术大学出版社,1993.
[21] 方二敏,胡正祥.安全管理学[M].北京:中国经济出版社,1991.
[22] 谢庆森,王秉权.安全人机工程[M].天津:天津大学出版社,1999.
[23] 曲和鼎.安全系统工程概论[M].北京:化学工业出版社,1988.
[24] 舒服华.改进支持向量机在矿井风机故障诊断中的应用[J].矿山机械,2006(3):39～41.
[25] 敬旭东,丁川,刘江.当前企业安全生产存在的问题及对策[J].中国安全科学学报,2005,23(11):35～36.
[26] 黄勇,毛保华.港口安全系统管理[J].中国安全科学学报,2006,18(1):10～16.
[27] 吴永富,方芳.港口生产管理[M].武汉:华中理工大学出版社,1989.
[28] 胡金柱.模糊决策与决策支持系统[M].武汉:华中师范大学出版社,1989.
[29] 王泽申.安全分析与事故预测[M].北京:北京经济学院出版社,1990.
[30] She L. A Conception of Research to Mechanism of Enterprise Crisis Early-waming[C]. Collection of ICMSE'96.
[31] Vapnik V N. Estimation of Dependencies Based on Empirical Data[C]. Berlin: Springer-Verlag 1982.
[32] Vapnik V N. The Nature of Statistical Learning Theory[M]. NY: Springer-Verlag, 1995.
[33] Cherkassky V, Mulier F. Learning from Data: Concepts, Theory and Methods[M]. NY: John Viley & Sons, 2005.
[34] 边肇祺。模式识别[M]。北京:清华大学出版社,1988
[35] Vapnik V N, Levin E, Le C Y. Measuring the VC-dimension of a learning machine [J]. Neural Computation, 1994, (6): 851—876.
[36] Burges C J C. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Disco-very, 1998, 2(2): 57—60.
[37] Boser B, Guyon I, Vapnik V. A training algorithm for optimal margin classifiers[C]. Fifth Annual Workshop on Computational Learning Theory. Pittsburgh: ACM Press, 1992.
[38] Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20: 273—297.
[39] Scholkopf B, Burges C, Vapnik V N. Extracting support data for a given task. In: Fayyad U M Uthurusamy R(eds.). Proc. Of First Intl. Conf. On Knowledge Discovery & Data Mining, AAA I Press, 1995: 262-267.
[40] Vapnik V N, Golowich S, Smola A. Support vector method for function approximation, regression estimation, and signal processing. In: Mozer M, Jordan M, Petsche T (eds)[C]. Neural Information Processing Systems, MIT Press, 1997, 9.
[41] Muller K-R, Smola A J, Ratsch G, et al. Predicting time series with support vector machines.In: Proc. of ICANN'97[C], Springer Lecture Notes in Computer Science, 1997, 999—1005.
[42] Zhang H B. Zhong P, Zhang C H. The Newton-PCG Algorithm via Automatic Differentiation. OR Transaction, 2003, 7(1): 28—38.
[43] Bartlett P L. The sample complexity of pattern classification with neural network: the size of the weights is more important than the size of network[J]. IEEE Transactions on Information Theory, 1998, 44(2): 525-536.
[44] Scholkopf B, Smola A, MllerK-R. Nonlinear componen tanalysis as kernel eigenvalu eproblem[J]. Neural Computation, 1998, 10(5): 1299—1319.
[45] Scholkopf B, Sung K-K, Burges C, et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers[C]. IEEE Trans. On Signal Processing, 1997, 45(11): 2758-2765.
[46] Scholkopf B , Burges C , Vapnik V. Incorporating invariances in support vector leamingmachines[A]. Malsburg C, von Seelen W, Vorbruggen J C. et al. Artificial Neural Networks-ICANN'96[C]. Berlin: Spingers Lecture Notes in Computer Science, 1996, 11(12): 47-52.
[47] Scholkopf B, Simard P, Smola A, et al. Prior knowledge in support vector kernels[C]. New York: NEPS'97, 1997.
[48] Burges C, Scholkopf B. Improving the accuracy and speed of support vector machines[A]. Mozer M, Jordan M, Petsche T. Neural Information Processing Systems[C]. Cambridge: MITPress, 1997.
[49] Shawe-Taylor J. Structural risk minimization over data-dependent hierarchies[J]. IEEE Transactions on Information Theory, 1998, 44(5): 1926—1940.
[50] Oren M, Papageorgiou C, Sinha P, et al. Pedestrian detection using wavelet templates[C] Puerto Rico: Proc.ofCVPR'97, 1997.
[51] Hearst M A, Scholkopf B, Dumais S, et al. Trends and controversies-support vector machines[J]. IEEE Intelligent Systems, 2005, 13(4): 18—28.
[52] Lu C Y, Yan P F, Zhang C S, et al. Face recognition using support vector machine[C]. Beijing Proc. of ICNNB'98, 1998: 652-655.
[53] Blanz V, Scholkopf B, Bulthoff H, et al. Comparison of view-based object recognition algorithms using realistic 3D models[A]. Malsburg C, Seelen W, Vorbrugen J C, et al(eds). Artificial Neural Networks——ICANN'96[C]. Berlin: Spingers Lecture Notes in Computer Science, 1996, 1112: 251～256.
[54] Anthony M, Bartlett P. Learning in Neural Networks, Theoretical Foundations[M]. Cambridge: Cambridge University Press, 1999.
[55] Mukherjee S, Osuna E, Girosi F. Nonlinear prediction of chaotic time series using a support vector machine[C]. New York: Proc. of NNSP'97, 1997.
[56] Kwok J, T-Y. Support vector mixture for classification and regression problems[C]. New York: ICPR'98, 1998.
[57] Bennett K, Mangasarian O. Robust linear programming discrimination of two linearly inseparable sets[J]. Optimization Method sand Software, 1992, 1: 23～3431.
[58] Osuna E, Freund R, Girosi F. An improved training algorithm for support vector machines[C]. NewYork: Proc. of NNSP'97, 1997.
[59] Bennett K P, Demiriz A. Semi-supervised support vector machines[C]. Berlin: Proc. of NIPS'98, 1998.
[60] Zhang XG. Using class-center vectors to build support vector machines[C]. Madison: Proc. Of NNSP'99, 1999.
[61] Anguita D, Ridella S, Rovetta S. Circuital implementation of support vector machines[J]. Electronics Letters, 1998, 34(16): 1596～1597.
[62] 谢天生,朱建华,吕广宇.港口机械安全性与港口生产安全[J].港口装卸,2005,18(5),119～120.
[63] 闫维明,何浩祥,马华等.基于粗糙集和支持向量机的空间结构健康监测[J].沈阳建筑大学学报,2006(1):86～90.
[64] 原峰山,朱思铭.基于动态数据的支持向量分类机算法实现[J].计算机工程与应用,2006,23(7),39～42.
[65] 张绍,张素,章琛曦.基于多核支持向量机的概率密度估计方法[J].计算机仿真,2006,23(1),85～88.
[67] 刘广利.基于支持向量机的经济预警方法研究[D].北京:中国农业大学,2003.
[68] 邓乃扬,田英杰.数据挖掘中的新方法——支持向量机[M].北京:科学出版社,2004.
[69] 史忠植.知识发现[M].北京:清华大学出版社,2002.
[70] 张恒喜,郭基联,朱家元,等.小样本多元数据分析方法及应用[M].西安:西安工业大学出版,2002.
[71] 朱家元,张喜斌,张恒喜,等.多参数装备费用的支持向量机预测[J].系统工程与电子技术,2005,25(6):701～703.
[72] Zhu J Y, Ren B, Zhang H X, et al. Time Series Prediction via New Support Vector Machines[C]. Beijing: IEEE In proceedings of ICMLC2002, 2002.
[73] Zhu J Y, Shang B L, Zhang H X. An Improve Fast Classification Algorithm Based on Leaming Vector Quantization[C]. Chengdu: In proceedings of international conference on modeling and simulating of complex system, 2002.
[74] 张学工译.统计学习理论的本质[M].北京:清华大学出版社,2000.
[75] 刘江华,程君实,陈佳品.支持向量机训练算法综述[J].信息与控制,2002,31(1),45～50.
[76] 朱国强,刘士荣,俞金寿.支持向量机及其在函数逼近中的应用[J].华东理工大学学报,2002 28(2),555～559.
[77] 孙国庆.港口安全管理现状分析及对策研究[J].中国安全科学学报,2005,19(7):40～43.
[78] 袁小芳,王耀南.基于混沌优化算法的支持向量机参数选取方法[J].控制与策,2006,21(1):111～117.
[79] 刘向东,朱美琳,陈兆乾,等.支持向量机及其在模式识别中的应用[J].计算机科学,2003,30(6):113～116.
[80] 杨静,张健沛,刘大昕.基于多支持向量机分类器的增量学习算法研究[J].哈尔滨工程大学学报,2006,12(2):103～106.
[81] 田盛丰,黄厚宽.基于支持向量机的数据库学习算法[J].计算机研究与发展,2000,37(1):17～22.
[82] 谷雨,赵佳枢,杨柽.基于负相关学习的支持向量机集成算法[J].微电子学与计算机,2006.23(3):58～61.
[82] Weston J, Watldns C. Multi-class Support Vector Machines[R]. London: In Royal Holloway University of London, 1998.
[83] 王晔,黄上腾.基于间隔区域样本数量的加权支持向量机[J].计算机工程,2006,32(6):31～33.
[84] 袁亚湘,孙文瑜.最优化理论与方法[M].北京:科学出版社,1997.
[85] 徐成贤,陈志平,李乃成.近代优化方法[M].北京:科学出版社,2002.
[86] 李东晖,杜树新,吴铁军.基于壳向量的线性支持向量机快速增量学习算法[J].浙江大学学报,2006,40(2):202～206.
[87] 龚灯才,孙长银,李林峰.基于模糊回归支持向量机的短期负荷预测[J].三峡大学学报,2006,28(1):37～40.
[88] 乔立岩,彭喜元,彭宇.基于微粒群算法和支持向量机的特征子集选择方法[J].电子学报,2006,34(3):496～498.
[89] 乔立岩,彭喜元,马云彤.基于遗传算法和支持向量机的特征子集选择方法[J].电子测量与仪器学报,2006,20(1):1～5.
[90] 王弄,经玲.用支持向量机方法实现曲面重构中的数据处理[J].电子与信息学报,2003,(25):52～56.
[91] 杨宏晖,孙进才,袁骏.基于支持向量机和遗传算法的水下目标特征选择算法[J].西北工业大学学报,2005,23(4):512～515.
[92] 冯瑞,张浩然,邵惠鹤.基于SVM的软测量建模[J].信息与控制,2002,31(6):567～571.
[93] 李路,袁震东.用遗传算法求SVM的最优超平面[J].上海工程技术大学学报,2005,19(1):21～23.
[94] 诸蕾蕾,陈绥阳,周梦等.计算智能的数学基础[M].北京:科学出版社,2002.
[95] Vapnil V. An Overview of Statistical Learning Theory[J]. IEEE Transactions on Neural Networks, 1999, 10(5): 988～999.
[96] 范听炜.支持向量机算法的研究及其应用[D].杭州:浙江大学,2003.
[97] 薛毅.支持向量机与数学规划[D].北京:北京工业大学,2003.
[98] 边肇祺,张学工.模式识别[M].北京:清华大学出版社,2003.
[99] 袁小芳,王耀南,孙炜.支持向量机-模糊推理自学习控制器设计[J].控制理论与应用,2006,23(1):1～6.
[100] 刘广利,邓乃扬.基于SVM分类的预警系统[J].中国农业大学学报,2002,7(6):97～100.
[101] 张仕华,王学业 增量回归支待向量机改进学习算法[J].计算机工程与应用,2006,23(1):40～42.
[102] 余艳芳,高大启.一种改进的最小二乘支持向量机及其应用[J].计算机工程与科学,2006,28(2):69～71.
[103] 裴学华,李朝峰.支持向量机和BP网络改进模型的性能对比研究[J].微电子学与计算机,2006,23(1):169～173.
[104] 田志勇.基于人工神经网络的施政安全评价决策支持系统原型研究[D].上海:同济大学 1999.
[2] 俞来宝.中国加入WTO后港口面临的机遇和挑战[J].中国港口,2005,18(11):20～21.
[3] 刘婷,魏国兴.港口安全生产特性分析[J].中国水运,2000,18(4):32～33.
[4] 余廉.企业预警管理论[M].河北:河北科学技术出版社,1999
[5] 陈宝智,王金波.安全管理[M].天津:天津大学出版社,1999
[6] 谢庆森,王秉权.安全人机工程[M].天津:天津大学出版社,1999
[7] 汪元辉.安全系统工程[M].天津:天津大学出版社,1999
[8] 谢进光.对当前港口安全管理工作的几点思考[J].中国港口,2001,12(8):12～13.
[9] 丁国良.交通运输企业经营管理[M].北京:人民交通出版社,1999
[10] 吴永富,方芳.港口生产管理[M].武汉:华中理工大学出版社,1989
[11] 宋德驰等.港口装卸工艺[M].北京:人民交通出版社,1994
[12] 施欣.水运企业现代管理理论方法与应用[M].北京:人民交通出版社,2000
[13] 杨步明,候世安,周通学等.劳动安全法规选编[R].山东:日照港务局安全生产委员会,1997
[14] 国家安全生产检查管理局、国家煤矿安全监察局.国家安全生产科技发展规划(交通运输建筑领域研究报告)(2004～2010)[R].2003.
[15] Everett J G, Thompson W S. Experience Modification Rating For Workers' Compensation Insurance[J]. Journal of Construction Engineering and Management, 2005, 121(1): 66～78.
[16] 顾培亮.系统分析[M].北京:机械工业出版社,1990.
[17] 顾培亮.系统分析与协调[M].天津:天津大学出版社,1998.
[18] 李哲,袁治平.建立递阶层次结构模型的方法探讨[J].系统工程理论方法应用,1995,4(2):71～73.
[19] 许绛垣,罗云,樊运晓.系统安全分析评价技术方法对比研究[J].劳动保护科学技术,1998,18(4):24～25.
[20] 陈晓剑,梁粱.系统评价方法及应用[M].北京:中国科学技术大学出版社,1993.
[21] 方二敏,胡正祥.安全管理学[M].北京:中国经济出版社,1991.
[22] 谢庆森,王秉权.安全人机工程[M].天津:天津大学出版社,1999.
[23] 曲和鼎.安全系统工程概论[M].北京:化学工业出版社,1988.
[24] 舒服华.改进支持向量机在矿井风机故障诊断中的应用[J].矿山机械,2006(3):39～41.
[25] 敬旭东,丁川,刘江.当前企业安全生产存在的问题及对策[J].中国安全科学学报,2005,23(11):35～36.
[26] 黄勇,毛保华.港口安全系统管理[J].中国安全科学学报,2006,18(1):10～16.
[27] 吴永富,方芳.港口生产管理[M].武汉:华中理工大学出版社,1989.
[28] 胡金柱.模糊决策与决策支持系统[M].武汉:华中师范大学出版社,1989.
[29] 王泽申.安全分析与事故预测[M].北京:北京经济学院出版社,1990.
[30] She L. A Conception of Research to Mechanism of Enterprise Crisis Early-waming[C]. Collection of ICMSE'96.
[31] Vapnik V N. Estimation of Dependencies Based on Empirical Data[C]. Berlin: Springer-Verlag 1982.
[32] Vapnik V N. The Nature of Statistical Learning Theory[M]. NY: Springer-Verlag, 1995.
[33] Cherkassky V, Mulier F. Learning from Data: Concepts, Theory and Methods[M]. NY: John Viley & Sons, 2005.
[34] 边肇祺。模式识别[M]。北京:清华大学出版社,1988
[35] Vapnik V N, Levin E, Le C Y. Measuring the VC-dimension of a learning machine [J]. Neural Computation, 1994, (6): 851—876.
[36] Burges C J C. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Disco-very, 1998, 2(2): 57—60.
[37] Boser B, Guyon I, Vapnik V. A training algorithm for optimal margin classifiers[C]. Fifth Annual Workshop on Computational Learning Theory. Pittsburgh: ACM Press, 1992.
[38] Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20: 273—297.
[39] Scholkopf B, Burges C, Vapnik V N. Extracting support data for a given task. In: Fayyad U M Uthurusamy R(eds.). Proc. Of First Intl. Conf. On Knowledge Discovery & Data Mining, AAA I Press, 1995: 262-267.
[40] Vapnik V N, Golowich S, Smola A. Support vector method for function approximation, regression estimation, and signal processing. In: Mozer M, Jordan M, Petsche T (eds)[C]. Neural Information Processing Systems, MIT Press, 1997, 9.
[41] Muller K-R, Smola A J, Ratsch G, et al. Predicting time series with support vector machines.In: Proc. of ICANN'97[C], Springer Lecture Notes in Computer Science, 1997, 999—1005.
[42] Zhang H B. Zhong P, Zhang C H. The Newton-PCG Algorithm via Automatic Differentiation. OR Transaction, 2003, 7(1): 28—38.
[43] Bartlett P L. The sample complexity of pattern classification with neural network: the size of the weights is more important than the size of network[J]. IEEE Transactions on Information Theory, 1998, 44(2): 525-536.
[44] Scholkopf B, Smola A, MllerK-R. Nonlinear componen tanalysis as kernel eigenvalu eproblem[J]. Neural Computation, 1998, 10(5): 1299—1319.
[45] Scholkopf B, Sung K-K, Burges C, et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers[C]. IEEE Trans. On Signal Processing, 1997, 45(11): 2758-2765.
[46] Scholkopf B , Burges C , Vapnik V. Incorporating invariances in support vector leamingmachines[A]. Malsburg C, von Seelen W, Vorbruggen J C. et al. Artificial Neural Networks-ICANN'96[C]. Berlin: Spingers Lecture Notes in Computer Science, 1996, 11(12): 47-52.
[47] Scholkopf B, Simard P, Smola A, et al. Prior knowledge in support vector kernels[C]. New York: NEPS'97, 1997.
[48] Burges C, Scholkopf B. Improving the accuracy and speed of support vector machines[A]. Mozer M, Jordan M, Petsche T. Neural Information Processing Systems[C]. Cambridge: MITPress, 1997.
[49] Shawe-Taylor J. Structural risk minimization over data-dependent hierarchies[J]. IEEE Transactions on Information Theory, 1998, 44(5): 1926—1940.
[50] Oren M, Papageorgiou C, Sinha P, et al. Pedestrian detection using wavelet templates[C] Puerto Rico: Proc.ofCVPR'97, 1997.
[51] Hearst M A, Scholkopf B, Dumais S, et al. Trends and controversies-support vector machines[J]. IEEE Intelligent Systems, 2005, 13(4): 18—28.
[52] Lu C Y, Yan P F, Zhang C S, et al. Face recognition using support vector machine[C]. Beijing Proc. of ICNNB'98, 1998: 652-655.
[53] Blanz V, Scholkopf B, Bulthoff H, et al. Comparison of view-based object recognition algorithms using realistic 3D models[A]. Malsburg C, Seelen W, Vorbrugen J C, et al(eds). Artificial Neural Networks——ICANN'96[C]. Berlin: Spingers Lecture Notes in Computer Science, 1996, 1112: 251～256.
[54] Anthony M, Bartlett P. Learning in Neural Networks, Theoretical Foundations[M]. Cambridge: Cambridge University Press, 1999.
[55] Mukherjee S, Osuna E, Girosi F. Nonlinear prediction of chaotic time series using a support vector machine[C]. New York: Proc. of NNSP'97, 1997.
[56] Kwok J, T-Y. Support vector mixture for classification and regression problems[C]. New York: ICPR'98, 1998.
[57] Bennett K, Mangasarian O. Robust linear programming discrimination of two linearly inseparable sets[J]. Optimization Method sand Software, 1992, 1: 23～3431.
[58] Osuna E, Freund R, Girosi F. An improved training algorithm for support vector machines[C]. NewYork: Proc. of NNSP'97, 1997.
[59] Bennett K P, Demiriz A. Semi-supervised support vector machines[C]. Berlin: Proc. of NIPS'98, 1998.
[60] Zhang XG. Using class-center vectors to build support vector machines[C]. Madison: Proc. Of NNSP'99, 1999.
[61] Anguita D, Ridella S, Rovetta S. Circuital implementation of support vector machines[J]. Electronics Letters, 1998, 34(16): 1596～1597.
[62] 谢天生,朱建华,吕广宇.港口机械安全性与港口生产安全[J].港口装卸,2005,18(5),119～120.
[63] 闫维明,何浩祥,马华等.基于粗糙集和支持向量机的空间结构健康监测[J].沈阳建筑大学学报,2006(1):86～90.
[64] 原峰山,朱思铭.基于动态数据的支持向量分类机算法实现[J].计算机工程与应用,2006,23(7),39～42.
[65] 张绍,张素,章琛曦.基于多核支持向量机的概率密度估计方法[J].计算机仿真,2006,23(1),85～88.
[67] 刘广利.基于支持向量机的经济预警方法研究[D].北京:中国农业大学,2003.
[68] 邓乃扬,田英杰.数据挖掘中的新方法——支持向量机[M].北京:科学出版社,2004.
[69] 史忠植.知识发现[M].北京:清华大学出版社,2002.
[70] 张恒喜,郭基联,朱家元,等.小样本多元数据分析方法及应用[M].西安:西安工业大学出版,2002.
[71] 朱家元,张喜斌,张恒喜,等.多参数装备费用的支持向量机预测[J].系统工程与电子技术,2005,25(6):701～703.
[72] Zhu J Y, Ren B, Zhang H X, et al. Time Series Prediction via New Support Vector Machines[C]. Beijing: IEEE In proceedings of ICMLC2002, 2002.
[73] Zhu J Y, Shang B L, Zhang H X. An Improve Fast Classification Algorithm Based on Leaming Vector Quantization[C]. Chengdu: In proceedings of international conference on modeling and simulating of complex system, 2002.
[74] 张学工译.统计学习理论的本质[M].北京:清华大学出版社,2000.
[75] 刘江华,程君实,陈佳品.支持向量机训练算法综述[J].信息与控制,2002,31(1),45～50.
[76] 朱国强,刘士荣,俞金寿.支持向量机及其在函数逼近中的应用[J].华东理工大学学报,2002 28(2),555～559.
[77] 孙国庆.港口安全管理现状分析及对策研究[J].中国安全科学学报,2005,19(7):40～43.
[78] 袁小芳,王耀南.基于混沌优化算法的支持向量机参数选取方法[J].控制与策,2006,21(1):111～117.
[79] 刘向东,朱美琳,陈兆乾,等.支持向量机及其在模式识别中的应用[J].计算机科学,2003,30(6):113～116.
[80] 杨静,张健沛,刘大昕.基于多支持向量机分类器的增量学习算法研究[J].哈尔滨工程大学学报,2006,12(2):103～106.
[81] 田盛丰,黄厚宽.基于支持向量机的数据库学习算法[J].计算机研究与发展,2000,37(1):17～22.
[82] 谷雨,赵佳枢,杨柽.基于负相关学习的支持向量机集成算法[J].微电子学与计算机,2006.23(3):58～61.
[82] Weston J, Watldns C. Multi-class Support Vector Machines[R]. London: In Royal Holloway University of London, 1998.
[83] 王晔,黄上腾.基于间隔区域样本数量的加权支持向量机[J].计算机工程,2006,32(6):31～33.
[84] 袁亚湘,孙文瑜.最优化理论与方法[M].北京:科学出版社,1997.
[85] 徐成贤,陈志平,李乃成.近代优化方法[M].北京:科学出版社,2002.
[86] 李东晖,杜树新,吴铁军.基于壳向量的线性支持向量机快速增量学习算法[J].浙江大学学报,2006,40(2):202～206.
[87] 龚灯才,孙长银,李林峰.基于模糊回归支持向量机的短期负荷预测[J].三峡大学学报,2006,28(1):37～40.
[88] 乔立岩,彭喜元,彭宇.基于微粒群算法和支持向量机的特征子集选择方法[J].电子学报,2006,34(3):496～498.
[89] 乔立岩,彭喜元,马云彤.基于遗传算法和支持向量机的特征子集选择方法[J].电子测量与仪器学报,2006,20(1):1～5.
[90] 王弄,经玲.用支持向量机方法实现曲面重构中的数据处理[J].电子与信息学报,2003,(25):52～56.
[91] 杨宏晖,孙进才,袁骏.基于支持向量机和遗传算法的水下目标特征选择算法[J].西北工业大学学报,2005,23(4):512～515.
[92] 冯瑞,张浩然,邵惠鹤.基于SVM的软测量建模[J].信息与控制,2002,31(6):567～571.
[93] 李路,袁震东.用遗传算法求SVM的最优超平面[J].上海工程技术大学学报,2005,19(1):21～23.
[94] 诸蕾蕾,陈绥阳,周梦等.计算智能的数学基础[M].北京:科学出版社,2002.
[95] Vapnil V. An Overview of Statistical Learning Theory[J]. IEEE Transactions on Neural Networks, 1999, 10(5): 988～999.
[96] 范听炜.支持向量机算法的研究及其应用[D].杭州:浙江大学,2003.
[97] 薛毅.支持向量机与数学规划[D].北京:北京工业大学,2003.
[98] 边肇祺,张学工.模式识别[M].北京:清华大学出版社,2003.
[99] 袁小芳,王耀南,孙炜.支持向量机-模糊推理自学习控制器设计[J].控制理论与应用,2006,23(1):1～6.
[100] 刘广利,邓乃扬.基于SVM分类的预警系统[J].中国农业大学学报,2002,7(6):97～100.
[101] 张仕华,王学业 增量回归支待向量机改进学习算法[J].计算机工程与应用,2006,23(1):40～42.
[102] 余艳芳,高大启.一种改进的最小二乘支持向量机及其应用[J].计算机工程与科学,2006,28(2):69～71.
[103] 裴学华,李朝峰.支持向量机和BP网络改进模型的性能对比研究[J].微电子学与计算机,2006,23(1):169～173.
[104] 田志勇.基于人工神经网络的施政安全评价决策支持系统原型研究[D].上海:同济大学 1999.