摘要
矿井提升机是矿井安全生产过程中关键设备之一,是集机械、电气、液压于一体的矿山大型复杂机电系统,其系统单元之间以及系统单元内部都存在很多错综复杂、关联耦合的相互关系,不确定因素和不确定信息充斥其间,其故障可能是多故障、关联故障等多种复杂形式。因此,故障诊断问题是目前提升机系统维护工作中的首要问题。针对这些问题,本文以数字化提升机故障诊断技术为核心开展相关的研究,其主要工作包括:
1.从提升机系统物理结构和逻辑组成出发,深入分析提升机故障模式和故障原因的传播机理,融合语义知识,对提升机系统单元及其故障领域知识进行分析,构建了语义环境下的矿井提升机故障诊断模型,提出了基于本体的提升机故障语义知识系统框架。
2.通过对提升机系统及其故障现象、故障原因的逻辑关系进行分析,提出基于描述逻辑的提升机故障知识表示方法,对提升机系统单元及其故障领域知识进行语义描述,并对所构建的故障知识库进行了逻辑检错推理。
3.为增强提升机故障语义知识库中故障规则更新完善的能力,应用粗集理论及属性约简方法,提出一种改进的故障规则更新算法,提高了提升机故障语义知识库中诊断规则的获取和更新的效率。
4.通过对故障诊断推理方法的分析,借鉴专家系统中模糊规则推理的思想,根据提升机故障诊断知识相似性强的特点,提出了一种基于语义相似度的提升机故障知识推理方法,增强了提升机故障语义知识模型的推理能力,并兼顾了规则匹配的效率和准确性要求。
5.通过构建提升机故障语义知识推理原型系统,从而实现了语义环境下提升机故障知识推理中故障知识的分析、转换和处理等关键问题,印证了提升机故障语义知识表示与推理方法的可行性和有效性,为提升机故障诊断方法提出了新的思路和新的探索。
Mine hoist is a large and complex system of machine, electric and hydraulic pressure, which is the key equipment in mine safety production. Because of uncertain factors the faults may be multiple failures, associated faults or other complex forms. Therefore, how to resolve the problems of fault diagnosis becomes the most important issue to system maintenance. Then, related application research is carried out and corresponding system is developed in the background of the digital hoist system, the main contents are as following:
1. By analyzing the fault pattern and fault transmitting mechanism and fusing semantic knowledge and ontology technology, fault diagnosis model is proposed in semantic environment, which mine hoist fault semantic knowledge system frame is put forward.
2. The knowledge describing method of mine hoist fault knowledge is proposed based on Description Logic by analyzing the logic relation between fault symptom and fault causes, and design the logic reasoning.
3. An improved fault rules update algorithm is put out in order to enhance the ability of updating and perfecting diagnosis rules in the mine hoist fault semantic knowledge base.
4. By analyzing fault knowledge reasoning methods, a hoist fault knowledge reasoning method is proposed based on semantic similarity, which enhances the reasoning ability of the hoist fault semantic knowledge model and takes into account the efficiency and accuracy requirements of the rule matches.
5. Mine hoist fault semantic knowledge system is building to solve the critical issues of analyzing, converting and processing the hoist fault knowledge in semantic environment, and it proves that the mine hoist fault semantic knowledge representation and reasoning methods are feasible and effective, and puts forward new ideas and new exploration for hoist fault diagnosis methods.
引文
[1]《矿井提升机故障处理和技术改造》编委会.矿井提升机故障处理和技术改造[M].北京:机械上业出版社,2005.1.
[2]煤矿安全规程[M].煤炭上业出版社出版,2001.12.
[3]赵富.矿井提升设备的技术改造[J].山东煤炭科技,2000(3):21-22.
[4] Frank P.M&Ding X.Survey of robust residual generation and evaluation methods in observer based fault detection systems[J].Journal of Process Control,1997,7(6):403-424.
[5]周东华,胡艳艳.动态系统的故障诊断技术[J].自动化学报.2009,35(6):748-758.
[6]王道平,张义忠.故障智能诊断系统的理论与方法[M].北京:冶金工业出版社.2001.
[7] Frank P M. Fault diagnosis in dynamics systems using analytical and knowledge-based redundancy: a survey and some new results [J]. Automatica, 1990, 26(3): 459-474.
[8] Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri S N. A review of process fault detection and diagnosis part I: quantitative model-based methods [J]. Computers and Chemical Engineering, 2003, 27(3): 293-311.
[9] Rolf Isermann. Model-based fault-detect ion and diagnosis–status and applications [J]. Annual Reviews in Control, 2005, 29:71–85.
[10] Drago R J. Incipient failure detection [J]. Power Transmission Design, 1979, 21(2): 40-45.
[11] Drif M, Benouzza N, Kraloua B, Bendiabdellah A, Dente J A. Squirrel cage rotor faults detection in induction motor utilizing stator power spectrum approach[C]. In: Proceedings of International Conference on Power Electronics, Machines, and Drives. London, UK: IEEE, 2002. 133-138.
[12] Xu Z&Zhao H. Design of fault detection and isolation via wavelet analysis and neural network[A].//IEEE International Symposium on Intelligent Control[C].Vancouver, Canada, 2002:467-472.
[13] Butler-Purry, K. L. & Bagriyanik, M. Characterization of transients in transformers using discrete wavelet transforms [J]. IEEE Transaction on Power Delivery, 2003, 18(2), 648–656.
[14] Chow M Y, Mangum P, Thomas R J. Incipient fault detection in DC machines using a neural network[C]. In: Proceedings of the 22nd Asilomar Conference on Signals, Systems, and Computers. San Jose, USA: IEEE, 1988. 706-709.
[15] Zhang J. Improved on-line process fault diagnosis through information fusion in multiple neural networks [J]. Computers and Chemical Engineering, 2006, 30(3): 558-571.
[16] Quteishat A, Lim C P. A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification [J]. Applied Soft Computing, 2008, 8(2): 985-995.
[17] Jack L B, Nandi A K. Support vector machines for detection and characterization of rolling element bearing faults [J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2001, 215(9):1065-1074.
[18] Castro D, Ranson A, Matheus J, Hemandez K. Fault detection and identification in chemical processes using multivariable statistical techniques and SVM for classification[C]. In: Proceedings of ISA Monterrey 2002. North Carolina, USA: ISA Services Inc, 2002. 165-175.
[19] Cherry G A, Qin S J. Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis [J]. IEEE Transactions on Semiconductor Manufacturing, 2006, 19(2): 159-172.
[20] Chen G, Yang Y W, Zuo H F. Intelligent fusion for aeroengine wear fault diagnosis [J]. Transactions of Nanjing University of Aeronautics and Astronautics, 2006, 23(4): 297-303.
[21]肖兴明.摩擦提升重大事故分析及预防[M].徐州:中国矿业大学出版社,1994.
[22]周瑾.矿井提升机故障机理与故障诊断研究[D].徐州:中国矿业大学,2001.
[23]贾现召,季晔,张步斌,李俊花.多绳摩擦式提升机主轴振动模态分析研究[J] .制造业自动化,2007(10): 228-232
[24]刘军.某矿井提升机主轴断裂事故分析.煤矿机械[J], 2005( 11): 113-116.
[25]王久凤,徐桂云,朱家洲,杨艳初.基于Pro/E的提升机主轴参数化设计及ANSYS有限元分析[J].煤矿机械,2008, 29(12) 162-166.
[26]王致杰,王耀才,李冬.基于小波网络的矿井提升机运行故障趋势预测研究[J].中国矿业大学学报,2001,34(4): 528-532.
[27]唐传贵.基于小波分析与最大熵谱分析的矿井提升机故障诊断[J].煤矿机械,2009, 30 (4): 282-285.
[28]马庆林,王新.提升机故障信号特征提取方法研究[J].煤矿机电,2009(2) 190-193.
[29]谷勇霞,谢远森.基于小波分析与Hilbert-Huang变换的矿井提升机故障诊断[J] .起重运输机械, 2009(6):23-25.
[30]彭观明,李志强,刘照军.用小波网络法预测提升机故障趋势[J].煤矿机械, 2007, 28(2) 211-214.
[31]周瑾,肖兴明.基于故障树和神经网络模型的提升机故障诊断专家系统[J].矿山机械,2004(5) 78-80.
[32]马小平,肖兴明.基于专家系统的提升机故障诊断系统[J].中国矿业大学学报, 1999,28(5): 499 -501.
[33]何风有,王磊,谭国俊等.提升机故障诊断专家系统研究[J].煤炭工程,2004(5):49-52.
[34]刘志海,徐文尚,鲁青.矿井提升机故障诊断专家系统的研究[J].煤矿机械,2006,27(5): 900 -902.
[35]陈志巧,王成刚,王致杰.矿井提升机故障智能诊断系统研究[J],煤矿机械, 2006, 27(5):910-912.
[36]王磊,马正兰,安忠林.基于CSCW的提升机故障诊断研究[J].中州煤炭,2004(3):7-8.
[37]杨淑珍,徐文尚,高云红.基于模糊推理的矿井提升机故障诊断方法的研究[J],煤矿机械,2005(9): 148-150.
[38]郭小荟,马小平.基于支持向量机的提升机制动系统故障诊断[J].中国矿业大学学报, 2006,35(6): 813-817.
[39] Ma Xian-Min.Application of ant colony algorithm in PID parameter optimization for mining hoist direct torque control system[J].Proceedings - International Conference on Advanced Computer Control, 2009:632-636.
[40] CHANG Yan-wei,WANG Yao-cai, LIU Tao,WANG Zhi-jie.Fault diagnosis of a mine hoist using PCA and SVM techniques[J]. Journal of China University of Mining and Technology, 2008.9, 3(18):327-331.
[41] XIA Shi-xiong,NIU Qiang,ZHOU Yong, ZHANG Lei.Mine-hoist fault-condition detection based on the wavelet packet transform and kernel PCA[J].Journal of China University of Mining and Technology,2008.12,4(18): 567-570 .
[42] Niu Qiang, Xia Shi-Xion,Zhou Yong,Zhang Lei. Fault condition recognition of mine hoist combining kernel PCA and SVM [J]. .IEEE International Conferenceon Integration Technology, 2007:643-647.
[43] Niu Qiang,Xia Shi-Xion,Zhou Yong,Zhang Lei.Fault condition detection based on wavelet packet transform and support vector data description[C]. Proceedings-2008 2nd International Symposium on Intelligent Information Technology Application, 2008(1):776-780.
[44]王秋彦,鞠建波,宋振宇.故障诊断技术研究现状及发展趋势[J].电子测量技术, 2009(4):5-8.
[45]何凤有,谭国俊.矿井直流提升机计算机控制技术[M].徐州:中国矿业大学出版社, 2003.
[46]林荣华.矿井提升机智能故障监控与诊断专家系统研究与实现[D],中国矿业大学,2004.
[47]秦绪平.矿井提升机安全控制策略研究[D].江苏:中国矿业大学,2006
[48]王劲.矿井提升机故障诊断与容错控制技术研究[D].江苏:中国矿业大学,2008.
[49]杨淑珍.基于模糊专家系统的矿井提升机故障诊断算法的研究[D],山东科技大学,2006,p9-10.
[50]马建民,赵增玉.现代提升机数字控制系统[M].中国矿业大学出版社,2002.
[51]郭文平.瑞典ABB公司矿井提升机安全保护系统分析[J].矿山机械,1997.6.
[52]李庆军,张立国,毛玉忠.瑞典ABB公司全自动矿井提升系统的研究与应用[J].水力采煤与管道运输,2003.6.
[53] ABB公司电力与自动化产品说明书[EB/OL].http://www.abb.com.cn/ProductGuide/
[54]马正兰.基于双PLC的提升机容错操作保护系统研究[D].江苏:中国矿业大学,2003.5
[55]李书光,胡家运.矿井提升机电控系统改造[J].煤矿机电,2001(1):42-43.
[56]赵炜平,于淑华.基于$7-300 PLC的提升机变频调速系统[J].煤矿机械,2008(9:120-122.
[57]西门子公司工业产品[EB/OL].http://w1.siemens.com/answers/cn/zh/index.htm?stc=cnccc020001
[58]中矿大传动与自动化有限公司产品介绍[EB/OL].http://www.zkcd.com.cn/Prclass.asp?id=80
[59]谭国俊,何凤有,周佩华.基于PLC控制的矿井提升机冗余操作保护系统[J]煤炭学报,1999.2,24(1).
[60]语义_百度百科[EB/OL]. http://baike.baidu.com/view/544098.htm?fr=ala0_1
[61]数据_百度百科[EB/OL]. http://baike.baidu.com/view/38752.htm
[62] Guus Schreiber著,史忠植,梁永全译.知识工程和知识管理[M].北京:机械工业出版社, 2003.
[63]史忠植.知识工程[M].北京:清华大学出版社,1988.
[64]安静.基于本体论的知识模型构建研究[D].吉林大学,2007.
[65]杜小勇,李曼,王珊.本体学习研究综述[J].软件学报,2006,17(9):1837-l847.
[66]李善平,尹奇韦华,胡玉杰,郭鸣,付相君.本体论研究综述[J].计算机研究与发展,2004,41(7):1041-1052.
[67]邓志鸿等.Ontology研究综述[J].北京大学学报(自然科学版),2002,38(5):730–738.
[68] NOY N.F. &MCGUINNESS D L.Ontology development: guide to creating your first ontologyER [D].Stanford: Stanford University, 2001.
[69]杨淑子等.基于知识的诊断推理[M].清华大学出版社,1993.
[70]周东华,叶银忠.现代故障诊断与容错控制[M].北京:清华大学出版社,2000.
[71]张跃,李德英.飞船系统的故障机理分析[J].系统工程理论与实践,1998(3):42-46.
[72]张跃,李德英,黄文虎.飞船系统故障诊断层次分解模型[J].系统工程理论与实践, 1996, 16 (10):39-43.
[73]顾芳.多学科领域本体设计方法的研究[D].北京:中国科学院研究生院,2004:14-16.
[74]陈壮生,瞿裕忠.基于本体的信息处理系统的设计与实现[J].计算机工程,2005,31(11):165-167.
[75] Haase Volker J.Sure Y Management of Dynamic Knowledge [J].Journal of Knowledge Management, 2005, 9(5):97-1 07.
[76]李飞,高济,OKMF:一个基于本体论的知识管理系统框架[J].计算机辅助设计与图形学学报,2003,15(12):1538-1543.
[77]石莲,孙吉贵.描述逻辑综述[J].计算机科学,2006,33(1):194-197.
[78] Franz Baader etal. The Description Logic Handbook:Theory,Implementation and Applications[M]. Cambridge University Press,2003.
[79] Ulrike Sattler. Description Logics for ontologies[C].In Aldo de Moor, Wilfried Lex, and Bernhard Ganter, editors, Proceedings of eleventh International Conference on Conceptual Structures(ICCS2003), volume 2746 of Lecture Notes in Computer Science. Dresden, Germany: Springer, 2003.7. 21-25: 602-607.
[80] Franz Baader, Ian Horrocks, and Ulrike Sattler. Description Logics as ontology languages for the semantic web[C]. In Dieter Hutter and Werner Stephan, editors, Festschrift in honor of J.H. Siekmann, volume 2605 of Lecture Notes in Artificial Intelligence. Springer,2005:228–248.
[81] FrankManola&Eric Miller.RDF Primer[Z].http://www.w3.org/TR/2004/REC-rdf-primer-20040210/,2007-10-15.
[82] W3C.RDF Vocabulary Description Language 1.0:RDF Schema[Z].http://-www.w3.org/TR/rdf-schema/,2007-11-9.
[83] Horrocks I,Patel-Schneider F.P,&Hannelen F. Reviewing the design of DAML+OIL:An ontology language for the semantic web[C].Eighteenth national conference on Artificial intelligence.Edmonton,2002:792-797.
[84] World Wide Web Consortium.OWL Web Ontology Language Overview[Z].http://www.w3.org/TR/2004/REC-owl-features-20040210/,2008-4-27.
[85] Ian Horrocks, Peter F. Patel-Schneider, Harold Boley, Said Tabet, Benjamin Grosof &Mike Dean. SWRL: A Semantic Web Rule Language Combining OWL and RuleML [Z]. http://www.w3.org/Submission/SWRL/, 2004-5.
[86] Lehti, P. &Fankhauser, P. XML data integration with OWL: experiences and challenges[C]. Proceedings of the 2004 International Symposium on Applications and the Internet (SAINT'04), Washington. 2004: 160-167.
[87] World Wide Web Consortium. OWL Web Ontology Language Overview [Z]. http://www.w3.org/TR/2004/REC-owl-features-20040210/,2008-04-27.
[88]关铭.以OWL DL及SWRL为基础建置推理原型系统一以大学排裸为例[D].中原大学,硕士学位论文.2004
[89]梁晟,付弘宇,李明树.语义Web规则标记语言OWLRule +的设计与实现[J].计算机研究与发展, 2004,41 (7) : 1088-1095.
[90]郭文英.基于SWRL推理的语义关联发现及其在本体映射与集成中的应用[D].浙江大学,2006.9.
[91]沈标正.电机故障诊断技术[ M].北京:机械工业出版社1996.
[92]刘振兴,张哲,尹项根.异步电动机的状态监测与故障诊断技术综述[J].武汉科技大学学报,2001,24(3):285-289.
[93]刘振兴.电机故障在线监测诊断新原理和新技术研究[D].华中科技大学,2004.4.
[94]刘锦波,张承慧等.电机与拖动[M].北京:清华大学出版社,2006.9.
[95] Ian Horrocks P F P2S, Bechhofer S, Tsarkov D. OWL rules: A proposal and prototype implementation. Web Semantics, 2005:23-40
[96] Knublauch H,et al. The Protege OWL Plugin: An Open Development Environment for Semantic Web Applications. 2004.
[97] Ian Horrocks&Ulrike Sattler.A tableaux decision procedure for SHOIQ[C]. Proc.of the 19th Int.Joint Conf.on Artificial Intelligence (IJCAI), 2005.
[98]史忠植,蒋运承,张海俊等.基于描述逻辑的主体服务匹配[J].计算机学报,2004,27(5):625-635.
[99]蒋运承,汤庸,王驹.基于描述逻辑的模糊ER模型[J].软件学报,2006,17(1),20-30.
[100]朱利君,张友华,李绍稳等.基于描述逻辑的领域本体知识逻辑检测[J].农业网络信息,2008,(9) :138-141.
[101]牛强,夏士雄,谭国俊,王志.基于描述逻辑的电机故障诊断知识表示与推理[J].小型微型计算机系统, 2009.5
[102]曾黄麟.粗集理论及其应用[M].重庆:重庆人学出版社,1998.
[103]王国胤.Rough集理论与知识获取[M].西安交通大学出版社,2001.
[104]张文修.粗糙集理论与方法[M].科学出版社,2001.
[105]刘清. Rough集及Rough推理[M].科学出版社,2001.
[106]苗夺谦,王珏.粗糙集理论中概念与运算的信息表示[J].软件学报,1999,10(2):113-116.
[107]刘清,刘少辉,郑非.Rough逻辑及其在数据约简中的应用[J].软件学报,2001,12(3):415-419.
[108]常梨云,王国胤,吴渝.一种Rough Set理论的属性约简及规则提取方法[J].软件学报, 1999, 10(11) : 1206-1211.
[109] Hu XiaoHua, Cercone N. Learning in relational databases: a rough set approach [J]. Computational Intelligence, 1995, 11(2):323-337.
[110]叶东毅,陈昭炯.一个新的差别矩阵及其求核方法[J].电子学报,2002, 30(7) : 1086-1088.
[111]杨明,孙志挥.改进的差别矩阵及其求核方法[J].复旦大学学报(自然版),2004, 43(5) : 865 - 868.
[112]王国胤.决策表核属性的计算方法[J].计算机学报,2003, 26(5):611-615.
[113]苗夺谦,胡桂荣.知识约简的一中启发式算法[J].计算机研究与发展,1999, 36(6) : 681 -684.
[114] Jelonek J, et al. Rough set reduction of attributes and their domains for neural networks [J]. Computational Intelligence, 1995, 11(2):338-347.
[115]叶东毅. Jelonek属性约简算法的一个改进[J].电子学报,2000.28(12):81-82.
[116]王国胤,于洪,杨大春.基于条件信息嫡的决策表约简[J].计算机学报,2002,25 (7):759-766.
[117]刘少挥,盛秋歌,吴斌,等Rough集高效算法的研究[J]计算机学报,2003, 26(5) : 524-529.
[118]张腾飞,肖健梅,王锡淮.粗糙集理论中属性相对约简算法[J].电子学报,2005,33(11): 2080 - 2082.
[119]刘宗田.属性最小约简的增量式算法[J].电子学报,1999, 27 (11):90- 98.
[120]杨明.一种基于改进差别矩阵的核增量式更新算法[J].计算机学报,2006, 29(3): 407-413.
[121]杨明.一种基于改进差别矩阵的属性约简增量式更新算法[J].计算机学报,2007, 30(5) : 815 -822.
[122]胡峰,代劲,王国胤.一种决策表增量属性约简算法[J].控制与决策,2007,22(3).第22卷第3期. 2007, 22(3):268-277.
[123]王宗军,李红侠,邓晓岚.粗糙集理论研究的最新进展及发展趋势[J].武汉理工大学学报, 2006, 28(1) : 43-52.
[124]崔明辉.基于粗糙集理论的智能故障诊断研究[D].山东大学,2007.
[125]汪文峰.基于粗糙集理论的装载机故障诊断研究[D] .华中科技大学,2005.
[126]武波,马玉祥.专家系统[M].北京理工大学出版社,2001.
[127] Skowron A, Rauszer C. The discernibility functions matrics and functions in information systems [C]. Intelligent Decision Support-Handbook of Applications and Advances of the Rough Sets Theory. Dordrecht: Kluwer Academic Publisher, 1992: 331-362.
[128] Fayyad U M,Piatetsky-Shapiro L, Smyth P, et al. Advances in knowledge discovery and data mining [C]. AAAI Press. Mcnlo Park: MIT Press, 1996.
[129] Cercone V, Tsuchiya M. Luesy editors introdunction [J]. IEEE Trans on Knowledge and Data Engineering, 1993, 5(6):901-902.
[130] Piatetsky-Shapiro L, Frawley W J. Knowledge discovery in database[C]. AAAI Press. Menlo Park: MIT Press, 1991.
[131] Zheng Z, Wang G Y, Wu Y. A rough set and rule tree based incremental knowledge acquisition algorithm [C]. Lecture Notes In Artificial Intelligence 2639. Chongqing: Springer-Verlag, 2003: 122-129.
[132]於东军,王士同,杨静宇.一种增量式规则提取算法[ J].小型微型计算机系统,2004, 25(1): 79-81.
[133] Ziarkow, Shun N. Data-based acquisition and incremental modification classfication rules [J]. Computational Intelligence, 1995, 11(2):357-370.
[134] Susmaga R. Experiments in incremental computation of reducts [C]. Rough Sets in Data Mining and Knowledge Discovery. Berlin: Springer-Verlag, 1998.
[135] Bazan B, Nguyen Hung Son, Nguyen Sinh Hoa. Rough set algorithms in classification problem [C]. Rough Set Methods and Applications. Heidelberg: Physica-Verlag, 2000: 49-88.
[136] Wang Jue, Wang Ju. Reduction algorithms based on discerniblility matrix: The ordered attributed method [J]. J of Computer Science and Technlology, 2001, 11 (6):489-504.
[137] Niu Qiang, Xia Shixiong, Tan Guojun Hu Zhuhui. An Improved Incremental Updating Algorithm for the Core Computation of Decision Table[C]. 2009 International Conference on Artificial Intelligence and Computational Intelligence.
[138]徐蔚鸿,谢中科,杨静宇等.两类模糊推理算法的连续性和逼近性[J].软件学报, 2004,15(10): 1485-1492.
[139]潘正华.模糊推理算法的数学原理[J].计算机研究与发展. 2008, (45): 165-168.
[140]刘峡壁.人工智能导论:方法与系统[M].北京:国防工业出版社, 2008.
[141]卢耀晖,曹立明.一种改进的基于相似度的模糊推理方法[J].福建电脑, 2008, (3): 84-85.
[142] Zadeh L A. Outline of a new approach to the analysis of complex systems and decision processes[J]. IEEE Trans. on Systems, Man, and Cybernetics, 1973, 3 (1): 28-44.
[143]汪德刚,孟艳平等.模糊相似度推理算法及其构造的模糊系统逼近性能的分析[J].工程数学学报, 2009, 26(3): 423-430.
[144]林宗振.σ模糊度、σ相似度及σ距离[J].暨南大学学报(自然科学版), 2002, 23(3): 8-14.
[145]罗承忠.模糊集引论[M].北京:北京师范大学出版社, 1989.
[146] Turksen I B, Zhao Z. An approximate analogical reasoning approach based on similarity measures[J]. IEEE Trans. on Systems, Man, and Cybernetics, 1988, 18(6): 1049-1056.
[147] Chen S M. A new approach to handling fuzzy decision-making problems[J]. IEEE Trans. on Systems, Man, and Cybernetics, 1988, 18(6): 1012-1034.
[148] Yeung D S, Tsang E C C. Improved fuzzy knowledge representation and rule evaluation using fuzzy Petri nets and degree of subsethood[J]. International Journal of Intelligent Systems, 1994. 9(12): 1083-1100.
[149]李海军.基于相似度的加权模糊推理方法[D].保定:河北大学, 2006.
[150] Corley C, Mihalcea R. Measuring the semantic similarity of texts[A]. Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment[C].Ann Arbor,2005:13-18.
[151] Protégé-2000.美国斯坦福大学医学院[Z].http://protégé.stanford.edn/,2006-07-03.
[152] Jena 2 inference support[Z]. http://jena.sourceforge.net/inference/,2006-04-20.
[153] Princeton University. WordNet–A lexical database for English[EB/OL]. http://wordnet.princeton.edu/