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智能测控系统结构与性能评价研究
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摘要
由于智能测控系统是涉及多学科、多专业领域,集测量、控制、管理等于一体的多功能、多任务的复杂系统,系统设计至关重要。面对多样化的需求环境和不断丰富的软硬件资源,目前越来越多地形成了针对不同应用的特定智能测控系统,这在一定程度上使智能测控系统通用结构研究在描述和求解时遇到了诸多困难。另一方面,现有的智能测控系统性能评价理论尚未成熟,其评价方案存在着较大的局限性。在这种背景下,本文对通用智能测控系统逻辑结构建模、系统智能性、评价指标体系建立和系统综合性能评价等方面进行了理论研究和实践探索,以期对智能测控系统可持续研究与发展提供依据,主要研究内容和创新点总结如下:
     首先研究了智能测控系统的内涵。智能测控系统的应用广泛性、设计复杂性、需求多样性等特点决定了智能测控系统结构构成的多样性和不确定性,论文从智能测控系统的本质特征出发,研究智能测控系统的功能,抽取一般规律,探讨了一种合理的广义智能测控系统,为智能测控系统结构构成与性能评价等深入研究奠定基础。
     提出了基于拟人理论的USR逻辑结构模型并应用到智能测控系统的建模之中。在全面考虑拟人理论特点和现阶段典型智能测控系统实例的基础上,利用拟人理论建立智能测控系统通用逻辑结构USR模型,给出了系统USR模型的定义,分析讨论了结构单元集U、服务集S、关系集R等模型三要素的内涵,并对USR模型的优点及所解决的问题进行了分析。USR模型将智能测控系统看作一个由各个部分构成的有机整体,从系统结构方面和运行机理上进行形式化描述,可作为系统深层次分析与评价的起点。
     研究了智能测控系统的智能性。智能是智能测控系统与一般测控系统的最大区别,论文剖析了智能测控系统的智能含义,从技术层面上阐述了智能化方法在感知系统、分析处理系统、知识系统及表达系统等结构单元中的体现典型应用,并以智能弧压调高系统为应用实例,具体分析了模糊控制智能算法在应用中的优越性。针对系统智能水平的评价问题,采用拟人的智商算法进行计算,探讨基于改进离差智商算法进行智能测控系统智能水平评价的方法,为深入理解智能特性提供了理论依据和技术手段。
     构建了智能测控系统综合性能评价指标体系。指标体系按照系统科学性、可操作性、定量指标与定性指标相结合的原则,以系统逻辑结构划分为基础,以智能测控系统综合性能为总目标,共包含6个决策指标和15个基础指标,该指标体系能够较为全面地反映智能测控系统的综合性能状况。论文阐述了各评价指标的测算方法并利用层次分析方法确定了各指标权重。
     提出了一种利用证据推理理论评价智能测控系统综合性能的方法。由于智能测控系统的性能评价问题属于复杂的多目标决策问题,基于智能测控系统具有评价信息多源性、参数不确定性和模糊性等特点,论文选取综合证据理论、效用理论、模糊理论等优点的证据理论ERA(Evidential Reasoning Approach)评价方法实现对智能测控系统性能的评价,该方法能够有效处理智能测控系统中含有不知道和不确定参数信息的情况。论文分析了评价算法,并建立了智能测控系统综合性能评价ERA模型,而且针对评价信息的多源性给出了相应的信息融合处理方法。
     最后,在上述逻辑结构分析与综合性能评价方法的指导下,应用两个典型智能测控系统实例分别进行研析和理论验证。选取油井作业智能监测系统实例进行逻辑结构分析,利用拟人理论建立了油井作业监测系统逻辑结构USR模型,验证了基于拟人理论建立的逻辑结构模型的有效性。同时以电脑鼠迷宫寻迹系统为背景,以本团队设计的三种电脑鼠迷宫寻迹系统方案为对象建立了证据理论ERA评价模型,进行了系统性能综合评价。研究表明,评价结果比较合理,证据理论ERA评价方法能够有效解决智能测控系统性能综合评价问题,并对相关领域以及智能测控系统进一步研究具有一定的意义。
As a large complex system with multi-function and multi-task, the intelligent measurement and control system is involved in a number of disciplines and professional fields, so the system design is very important. With the diversified requirements and the increasingly enriched software and hardware resources, more and more specific intelligent measurement and control systems have been formed for different applications, which makes the related theory research of general structure about the intelligent measurement and control system very difficult. On the other hand, the exiting performance evaluation theory of intelligent measurement and control system is still not mature, and the exiting evaluation solutions have some limitations. In this background, this paper studies the theory and practices exploration of the logical structure modeling, the intelligent performance, the establishment of evaluation index system and the comprehensive performance evaluation of intelligent measurement and control system, in order to provide basis for sustainable research and development of system. The main research contents and abstracted innovative points are as follows:
     Firstly, the paper has discussed the connotation of the intelligent measurement and control system. Its universal applications, complex designs, diverse demands and such characteristics have decided the diversity and uncertainty of system structure composition. From the essential characteristics, this paper studies the system function, extracts the general rules, and expounds a kind of reasonable definition for generalized intelligent measurement and control system, which lays a foundation for the profound research of its structure composition and performance evaluation.
     This paper proposes a general logical structure USR model of intelligent measurement and control system based on the humanized theory. Considering the characteristics of humanized theory and the present typically examples of the intelligent measurement and control system, this paper establishes the USR model by the humanized theory, gives the definition of USR system model, and discusses the connotation of the three model elements including structural unit set U, service set S, relationship set R, etc. The merits and the problems to be solved by this model are analyzed. It takes the intelligent measurement and control system as an organic integrity constituted by each part, and makes the formalization descriptions by the system structure and operation mechanism, which can be used as a starting point of the high-level analysis and evaluation of system.
     This paper studies the intelligence characteristic. An intelligent measurement and control system can be distinguished from a general measurement and control system by the intelligence characteristic. This paper analyzes the intelligence connotation and expounds the typical applications of the intelligent methods to structure units such as the sense unit, the analysis processing unit, the knowledge system and the expression unit, etc. With the intelligent arc pressure adjustable high system as an application example, this paper explains the application of intelligent fuzzy control algorithm. With respect to the evaluation of system intelligent level, this paper carries on the humanized algorithm, and discusses the intelligent level evaluation method for intelligent measurement and control system based on the improved deviation intelligence quotient algorithm, which provides the theory basis and technology method for thoroughly understanding of intelligent characteristic.
     This paper builds up a new comprehensive performance evaluation index system for the intelligent measurement and control system. According to the principle of system scientificity, operationality, combination of quantitative index and qualitative index, this index system contains6decision indexes and15basic indexes on the basis of division of system logical structure. The index system can fully reflect its comprehensive performance. Then this paper expounds the calculation methods of each evaluation index and determines the weight of each index by AHP method.
     This paper proposes a system performance evaluation method by the D-S evidence reasoning theory. This performance evaluation of intelligent measurement and control system belongs to a complex multi-objective decision problem. Based on the characteristics of system such as assessment information polyphyletism, parameter uncertainty and parameter fuzziness etc, this paper takes ERA theory to evaluate, which has the advantage of D-S theory, utility theory, and fuzzy theory etc. This evaluation method can effectively deal with the problem with the unknown and uncertain parameter information of intelligent measurement and control system. This paper analyses the evaluation algorithm, and establishes the ERA comprehensive performance evaluation model. The response information fusion processing method is given with respect to the assessment information polyphyletism.
     Finally, in the direction of above logic structure analysis and comprehensive performance evaluation method, this paper selects two typical examples of the intelligent measurement and control system for the analysis and theoretical verification. The well operation intelligent monitoring system is selected as an example to analyze the logic structure, and the logic structure USR model is established by the humanized theory, which verifies the effectiveness of the logic structure model based on humanized theory. At the same time, taking computer mouse labyrinth tracing system as the background, this paper selects three kinds of design plans designed by our team as examples to establish the evidence theory ERA evaluation model and finish system comprehensive evaluation. Research shows that the evaluation result is reasonable, and this ERA evaluation method can effectively solve the problem of the performance comprehensive evaluation of intelligent measurement and control system, which has certain significance for the further research of intelligent measurement and control system and related fields.
引文
[1]沈兰荪.仪器仪表智能化的进展.测控技术.1999年18卷第1期:10-12
    [2]曾孟雄,李力,肖露等.智能检测控制技术及应用.电子工业出版社.北京:2008(6):1-36
    [3]丁建强.智能测控系统设计.知识经济.2011年2期:124
    [4]杨明,曾捷.自动测试系统中的智能结构.电子质量.2008年03期:53-56
    [5]涂序彦.柔性智能测控系统.测控技术.1999年18卷第8期:5-8
    [6]盛万兴,戴汝为.关于智能控制.电子学报.1999年5月第5期:86-89
    [7]赖根,肖明,清夏锐等.国外自动测试系统发展现状综述.探测与控制学报.2005年8月第27卷第3期:26-30
    [8]孙柏林.从美国“军事转型”看测控技术的发展趋势.测控技术.2005年第24卷第4期:1-5
    [9]杨晓华,沈珍瑶.智能算法及其在资源环境系统建模中的应用.北京师范大学出版社.2005.07:1-29
    [10]Fu K S. Learning control systems and intelligent control system:An intersection of artificial intelligence and automatic control. IEEE Trans.AC,1971,16(1):70-72
    [11]韦巍,何衍.智能控制基础.清华大学出版社.北京.2005,10:2-1]
    [12]李衍达.信息技术革命与测控技术的发展.测控技术.1996,02:29
    [13]提供用得起的先进测控技术—记“凌华2004测控技术先锋论坛”.今日电子.2004(05):2
    [14]吴饮伟.信息时代的工业仪表与控制系统.自动化仪表.2003,9
    [15]陈国金.工业过程监控_基于主元分析和盲源信号分析方法.浙江大学博十学位论文.2004,4
    [16]陈仁际,谈大龙等.基于Agent的机器人新型控制器模型研究.高技术通讯.2000(10):59-63
    [17]林海波,张十超,刘尊民等.联合站电量采集系统的设计与实现.微计算机信息.2008年第24卷第2-1期:93-94
    [18]王建平,王建华,胡小佳.基于智能体的武器装备体系评价模型研究.系统仿真学报.Jan.2009:15-18
    [19]于志坚.我国航天测控系统的现状与发展.中国工程科学.2006年10月,Vol.8,No.10:42-46
    [20]夏南银.航天测控系统.北京:国防工业出版社,2002
    [21]于英杰.现代测控技术的发展及应用.中国市场.2006年14期:88
    [22]顾基发.评价方法综述.系统工程与科学决策.北京:中国科技技术出版社.1990
    [23]张于心,智明光.综合评价指标体系和评价方法.北方交通大学学报.1995,9:393-400
    [24]姜晓鹏.可重构制造系统性能综合评价研究.西北工业大学博十学位论文.2007,4
    [25]郝海,踪家峰.系统分析与评价方法.经济科学出版社.2007,12:37-150
    [26]苏为华.多指标综合评价理论与方法问题研究.厦门大学博十学位论文.2000,9
    [27]万艮海.产品可靠性评估中的多源信息融合技术研究.合肥工业大学博士论文.2006,6
    [28]滕召胜,罗隆福,童调生.智能检测系统与数据融合.北京:机械工业出版社,1999
    [29]潘泉,于昕,程咏梅等.信息融合理论的基本方法与进展.自动化学报,2003,29(4):599-615
    [30]邓遂,曹红兵,沈杰等.基于传感器节点可靠性模型的协同决策算法.华中科技大学学报.2012年4月:63-66
    [31]周若.测控系统的可靠性.测控技术.1997年第16卷第5期:54-56
    [32]邹世超,孙长嵩.舰艇多功能指控平台硬件可靠性研究与分析.火力与指挥控制.2006年10月:54-57
    [33]李龙根,徐静.测量与控制系统精度评价的熵方法.现代制造工程2005增刊:108-109,129
    [34]Paterson J. Measuring Low Observable Technology's Effects on Combat Aircraft Survivability. AIAA 975544,1997
    [35]Whitmoyer R A. Techniques for Evaluating the Contributions of Avionics Subsystems to Fighter Aircraft Operational Effectiveness. W95042, CAIC,1991:446-453
    [36]韩伟杰,阎慧,王宇.航天测控系统容灾能力评估方法研究.计算机技术与发展.2011年8月:223-227
    [37]陈希祥,邱静,刘冠军.基于层次分析法与模糊综合评判的测试设备选择方法研究.兵工学报,2010,31(1):68-73
    [38]徐巍,谭德荣,文昌俊.测控系统软件质量模型及评价.计算机测量与控制.2005,13(8):858-880
    [39]顾凯平.复杂巨系统研究方法论.重庆出版社,1992:44-46
    [40]刘晓东,李亚荣,杨宝清.智能测控系统的基本结构构成.大连交通大学学报,2010年10月第31卷第5期:82-85
    [41]Tu xuyan. AI, AL and Robotics. Proceedings of FIRA World Congress (Plenary Speech),2002
    [42]涂序彦.大系统控制论.国防工业出版社.1994,2
    [43]Peuquet, D. J. It's about time:a conceptual framework for the representation of temporal dynamics in geographic information systems. Annals of the Association of American Geographers,1994(3):441-461
    [44]涂序彦,王枞,郭燕慧等.大系统控制论.北京:邮电大学出版社,2005,8
    [45]高旸.拟人多智体系统体系结构和协调策略的研究.北京邮电大学硕十论文,2006
    [46]钱学森,于景元,戴汝为等.一个科学新领域—开放的复杂巨系统及其方法论.北京:自然杂志,1990,13(1):3-10
    [47]周登勇,戴汝为.人工生命.模式识别与人工智能.1998,11(4):413-418
    [48]杨明.人工生命评述.电脑学习.2002年2月第一期:2-3
    [49]http://baike.baidu.com/view/1544351.htm
    [50]http://cz2010.qlteacher.com/submission/shengwu/6041428
    [51]韩立群.人工神经网络理论、设计及应用.化学工业出版社.2002
    [52]孟宪宇,涂序彦.广义人工生命概述.计算机应用研究.2006,2:4-6,11
    [53]白凤双,尹怡欣,涂序彦等.拟人控制系统的结构分析.微计算机信息.2006年第22卷第6-1期:16-18,169
    [54]蔡自兴.拟人控制方法.北京:化学出版社.2005(7):3-20
    [55]D. A. White, D. A. Sofge. Handbook of intelligent control, neural, fuzzy and adaptive approaches. Van Nostrand,1992
    [56]戴汝为,王珏.关于智能系统的综合集成.中科院自动化所人工智能实验室技术报告.1993
    [57]Development ways of intelligent measurement control system Sachenko, Anatoly Proceedings-IEEE Convention of Electrical & Electronics Engineers in Israel,1995:1-5
    [58]王瑞芳,刘林,徐方.机器人系统的故障预测技术研究制造业自动化.2008年第30卷第11期
    [59]王毅,侯雄坡,钟国辉.人工智能技术在仪器与测量中的应用.西安交通大学学报.2002(3):318-321,327
    [60]Borovyi, Andrii. Analysis of circuits for measurement of energy of processing units.2007 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems:Technology and Applications, IDAACS,2007:42-46
    [61]张召,张焕春.分布式测控系统模型的研究.计算机测量与控制.2002,10(2):107-111
    [62]易勇,吴波,古天祥.分布式测控网络系统EFMs模型研究.电子测量与仪器学报,2005,19(6):79-83
    [63]Kevin M. Passino, Bridging the Gap between Conventional and Intelligent Control, IEEE Control Syst. Mag., June 1993:12-18
    [64]P. J. Antsaklis and K. M. Passino, Eds., An Introduction to Intelligent and Autonomous Control. Norwell, MA:Kluwer,1993
    [65]Special Section on Intelligent Control, IEEE Control Syst. Mag., Vol.12, No.3, June 1992:71-94
    [66]Special Issue on Intelligent Control, IEEE Control Syst. Mag., Vol.11, No.4, June 1991:5-55
    [67]Special Section on Intelligent Control, IEEE Control Syst. Mag., Vol.11, No.1, Jan 1991:33-46
    [68]K. P. Valavanis and G. N. Saridis, Intelligent Robotic Systems. Theory, Design, and Applications. Norwell, MA:Kluwer,1992
    [69]D. A. White and D. A. Sofge, Eds., Handbook of Intelligent Control:Neural, Fuzzy, and Adaptive Approaches. New York:Van Nostrand Reinhold,1992
    [70]涂序彦,尹怡欣.人工生命及应用.北京邮电大学出版社,2004
    [71]刘东,尹怡欣,涂序彦.智能系统的广义智能定性评价之研究.计算机科学.2007,Vol.34,No.9:167-169
    [72]R. C, Luo. Multisensor integration and fusion in intelligent systems. IEEE transactions on systems, man and cybernetics,1999,27(5):774-779.
    [73]王雪.测试智能信息处理.北京:清华大学出版社.2007:20-37
    [74]王征,刘宁庄,张建成.数据融合的方法及应用研究.自动化与仪器仪表.2006.4:77-80
    [75]GaoJB, HarrisCJ. Some Remarkson Kalman Filters for the Multisensor Fusion. Information Fusion, 2002(3):191-200
    [76]伍奎,谢永春,李润方等.智能化系统的有机组合推理及优化.重庆大学学报.2003,26(6):1-5
    [77]伍奎,李润方,刘景浩等.智能化系统的知识表达与推理机制.机械工程学报.2005,5:98-102
    [78]史忠植.知识发现.北京:清华大学出版社,2002
    [79]Shin K G, Cwi X Z. Design of a knowledge based controller for intelligent control systems. IEEE trans. on S. M. C.1991, smc-21:368-375
    [80]Boudaound A H, Masson M H, Dubuisson B. On-line diagnosis of technological system:a fuzzy pattern recognition approach. In Proc. Of IFAC World Congress, San Francisco, USA,1996:103-107
    [81]孔德杰,张光先,乔立强等.数控等离子切割技术在我国的现状与发展.电焊机.2005,35(1):6-7,38
    [82]刘晓东,李亚荣,杨宝清等.基于模糊理论的多CPU智能弧压调高系统设计.制造技术与机床.2012年第9期
    [83]蒋翔俊,张优云,邹爱成等.模糊控制在数控火焰切割机自动调高系统中的应用.制造技术与机床,2008(6):87-89
    [84]李世勇.模糊控制、神经控制和智能控制论.哈尔滨工业大学出版社,1996:250-280
    [85]张文武,程良伦,薛航等.混合遗传算法优化的等离子切割机弧压调高控制研究.组合机床与自动化加工技术,2011(10):64-68
    [86]A. M. Turing. Computing Machinery and intelligence. Edited by John Hugeland. Mind Design Ⅱ: Philosophy, psychology, Artificial Intellilence. London:MIT press,1950:29-56
    [87]Rolf Herken. The universal Turing machine:a half-century survey. New York:Springer,1995:81
    [88]陆汝钤,韦梓楚,张松懋等.图灵测试—机器是否有智能.创新科技,2008年12期:52-53
    [89]樊成立.智能控制系统的智能水平的评价算法的研究.北京化工大学硕士研究生学位论文.2007,6
    [90]樊成立,刘东,尹怡欣.智能控制系统的智商评测算法和实现.微计算机信息.2007年第23卷第6-2期:253-255
    [91]刘东,尹怡欣,涂序彦等.一种智能控制系统智能水平的评价方法.中南大学学报.2005,8,第36卷第1期:14-17
    [92]刘东,尹怡欣,涂序彦等.智能系统智能特性的定性评价.计算机工程与应用.2007,43(11):1-3
    [93]Liu Dong, Yin Yi-xin, Dong Jie, etal. Calculating method of evaluating the intelligence quality of intelligent control systems.2006 IEEE International Symposium on Intelligent Control, Munich,2006
    [94]Wang Wei- xiao, Du Jun- ping, Tu Xu- yan. The study on the criterion and standard of intelligent system. Oct.2004:27-30
    [95]刘东,尹怡欣,涂序彦.从拟人的角度谈控制系统智能水平的评价.微计算机信息.2007年第23卷第9-2期:268-270
    [96]王新华,李堂军,丁黎黎等.复杂大系统评价理论与技术.山东大学出版社.2010.7:39-59,147-237
    [97]林森.复杂系统评价方法研究-以科研系统评价为例.青岛大学硕士学位论文.2007,10
    [98]Herrera F, Martinez L. A Model Based on Linguistie 2-Tuples for Dealing with Multigranular Hierarchical Linguistic Contexts in Multi-expert Decision-Making. IEEE Transaetions on Systems, Man and Cyberneties. Part B:Cybernetics,2001,31(2)
    [99]戴汝为.系统科学及系统复杂性研究.系统仿真学报,2002,14(11)
    [100]金菊良,魏一鸣.复杂系统广义智能评价方法与应用.北京:科学出版社.2007:29-102
    [101]林闯.计算机网络和计算机系统的性能评价.清华大学出版社.2001,4
    [102]Agogino A, Turner K. Entropy based anomaly detection applied to space shuttle main engines. Proceedings of the IEEE Aerospace Conference. Washington:IEEE Computer Society,2006
    [103]胡永宏.对TOPSIS法用于综合评价的改进.数学的实践与认识,2002,7:572-575
    [104]Hsu TsuenHo, Lin TsuenHo. QFD with Fuzzy and Entropy weight for evaluating retail customer values.2006
    [105]王贵宝.感知信息熵测度及其在可靠性工程中的应用研究.博士学位论文.电子科技大学.2009
    [106]Shannon C E. A mathematical theory of communication. Bell System Technical Journal,1948, 27:379-423,623-656
    [107]邵锡军,王宏飞.战场传感器能力指数分析模型.火力与指挥控制,2008,6第3卷第六期:111-113
    [108]刘宗斌,荆继武,夏鲁宁BLAKE算法的硬件实现研究.计算机学报,2012年4月:703-710
    [109]Gaj k, Homsirikamol E, Rogawski M. Comprehensive comparison of hardware performance of fourteen round 2 SHA-3 candidates with 512-bit outputs using field programmable gate arrays, Proceedings of the 2nd SHA-3 Candidate Conference, Santa Barbara,2010
    [110]许晓炜,李明禄,翁楚良.基于指令相对吞吐率的虚拟机检测方法.计算机工程.2011年6月:287-290
    [111]张卫杰,汤俊,许稼等.实时SAR成像处理器系统结构评估.2006年第46卷第7期:1227-1230
    [112]NUREG-0800, BTP7-21. Guidance on Digital Computer Real-Time Performance
    [113]汪绩宁,周爱平,郄永学.核电厂反应堆保护系统紧急停堆响应时间分析及测试.核动力工程.2012,4:5-10
    [114]Muller-Wihards D. Problem size scaling in the presence of parallel overhead. Parallel Computing, 1991(17):1361-1376
    [115]Muller-Wichards D, Ronsch W. Scalability of algorithms:An analytic approach. Parallel Computing.1995(21):937-952
    [116]王欣,姚佩阳,周翔翔等.指挥信息系统网络信息传输能力评什.计算机应用.2011,8:2033-2036
    [117]鞠九滨,杨鲲,徐高潮等.使用资源利用率作为负载平衡系统的负载指标.软件学报.1996,4:238-243
    [118]Mehra P. Wah B W. Automatic learning of workload measures for load balancing on a distributed system. Int'Iconf. on Parallel Processing,1993
    [119]Banawan S A, Zahorjan J. On comparing load indices using oracle simulation.1990 Winter Simulation Conference,1990
    [120]凌晓冬,刘冰,李宇波.多星测控调度效能评价指标体系研究.数学的实践与认识.2012,2:52-60
    [121]王景光.信息系统结构复杂性与系统可靠性关系研究.测控技术.2001年第20卷第2期:50-53
    [122]孟燕.铁路智能运输系统结构设计方法研究.铁道科学研究院博士学位论文.2004
    [123]陈晓彤.可靠性实用指南.北京:北京航空航天大学出版社,2005
    [124]王妍.轨道交通计算机平台可靠性与可用性分析.现代城市轨道交通.2011,4:93-98
    [125]王与力,杨晓东.一种更有效的并行系统可扩展模型.计算机学报.2001年1月:84-90
    [126]陈军,李晓梅.近优可扩展性:一种实用的可扩展性度量.计算机学报.2001年2月:179-182
    [127]Chen Jun, Li Xiao-Mei. A practical scalability metric. Proceedings of the HPC Asica 2000, 2000:403-404
    [128]窦亚玲,张友生.软件体系结构的可维护性评估研究.计算机工程与应用.2006,02:52-54
    [129]曲玉祥,吴(?).基于不完全维护的劣化系统分阶段顺序预防维护策略.机械工程学报.2011年5月:164-169
    [130]BROWN M, PROSCHAN F. Imperfect maintenance. Journal of Applied Probability,1983, 20(4):851-859
    [131]CASSADY C R, POHL E A, MURDOCK W P. Selective maintenance modeling for industrial systems. Journal of Quality in Maintenance Engineering,2001,7(2):104-117
    [132]康胜武,王应明,蔡志峰.基于粗糙集和模糊集理论的规则提取方法.厦门大学学报.2002,3,Vo1.41:173-176
    [133]Sugeno M, Kang G T. Structure identification of fuzzy model. Fuzzy Sets and System,1988, 28:15-33
    [134]Jang J. ANFIS:Adaptive-network-based fuzzy inference system. IEEE Trans. On SMC,1993, 23(3):665-685
    [135]Yinghua L, et al. Nonlinear system input structure identification:two stage fuzzy curves and surfaces. IEEE Trans. On SMC part A,1998,28(5):678-684
    [136]Magne S, Huns R. Transparent fuzzy modeling using fuzzy clustering and GA's. IEEE Trans. Part C,1999:198-202
    [137]焦李成.神经网络系统理论.西安:西安电子科技大学出版社,1990
    [138]Martin T H. Neural Network Design. PWS Publishing Company,1996
    [139]HuadongWu, etal. Sensor fusion using Dempster-Shafer theory, the Proeeedings of IEEE Instrumentation and Measurement Technology Conference, Anchorage, AK, USA, May21-23,2002
    [140]戴冠中,潘泉,张山鹰等.证据推理的进展及存在问题.控制理论与应用,1999,16(4):465-469
    [141]秦良娟.证据理论在复杂系统可靠性评价中的应用.西安交通大学学报,1998,32(8):100-103
    [142]沈瑞光,谢新连.不确定多属性决策方法在游船母港选址中的应用.大连海事大学学报.2007年2月
    [143]Jian-Bo Yang, Dong-Ling Xu, Kwai-Sang Chin. Intelligent Decision System for Supplier Assessment. Decision Support in an Uncertain and Complex World:The IFIP TC8/WG8.3 International Conference 2004:847-860
    [144]Dong-Ling Xu, Jian-Bo Yang, Intelligent Decision System based on the evidential reasoning approach and its applications. Journal of Telecommunications and Information Technology.2005,03:73-80
    [145]马费成,靖继鹏.信息经济分析.北京:科学技术文献出版社,2005
    [146]Von Neuman n J, Morgenstern O. Theory of Games and Economic Behavior. Princeton:Princeton University Press,1947
    [147]刘业政,姜元春,张结魁等.证据信度的效用分析.系统工程理论与实践.2008年3月:103-110
    [148]LIU J, YANG J B, WANG J, etal. Fuzzy rule based evidential reasoning approach for safety analysis[J]. International Journal of General System,2004,33(2):183-204
    [149]YANG J B, SINGH M G. An evidential reasoning approach for multiple criterion decision making with uncertainty. IEEE Transactions on System, Man and Cybernetic,1994(1):1-18
    [150]YANG J B. Rule and utility based evidential reasoning approach for multicriterion decision analysis under uncertainties. European Journal of Operational Resear ch,2001,131 (1):31-61
    [151]冯静,刘琦,周景伦等.航天产品多源可靠性信息的收集与整理.电子产品可靠性与环境试验.2002,12.No.6:48-53
    [152]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.长沙:国防科技大学出版社,1998
    [153]李萍.基于多CPU的油井作业监测系统的设计与实现.化工自动化及仪表.2010,37(2):44-47

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