注塑模改模知识的增量式发现研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
塑件产品的质量主要依赖于注塑模具,由于塑件产品结构多样复杂、一般为单件定制,即使正确应用模具设计制造原则也难以确保一次试模成功,不可避免地需要对模具进行修改(即改模)。而改模涉及模具设计制造过程中的许多环节,要求改模工程师不仅要掌握模具的设计和工艺知识,还包括课本上没有的各种工艺经验性知识。这些经验性知识目前只能通过不断实践,接受教训,逐步积累与提高。为此,许多模具企业已经开始注意收集整理相关的历史记录,以“问题与解决方案”的形式加以文档化,形成电子化的“改模方案”。其主要作用是在改模工程师制订改模方案时能够通过相似问题搜索,查找相似改模案例。这样可以在一定程度上改善改模的效率和效果,但随着案例数量的不断增加,可借鉴的方案越来越多况且有些问题看似相似,解决方案却又完全不同,改模工程师往往望而却步,迷失在成千上万的“案例”之中。走出上述困境的唯一方法是从这些历史方案中归纳出其内在规律,形成真正意义上的“经验性知识”资产。获取上述经验性知识的难点主要在于以下三个方面:首先,改模方案特征属性的不同取值之间存在着复杂的语义关联关系。如何建立面向改模方案的特征属性值之间的复杂关联模型,并从中抽取和归纳出规律,将成为获取改模知识的难点所在。其次,改模经验性知识具有多样性与不确定性,使得归纳总结改模方案中存在的内在规律难以入手。最后,实际应用要求模具知识管理系统必须具备可持续归纳能力。由于新增的改模案例会对原有的数据决策表产生影响,需要不断从当前的改模案例中抽取新的规律,归纳新的知识。因此,如何应用新的改模信息,建立可持续的学习机制,将是又一难点。
     针对上述应用问题与难点,本文做了如下四个方面的研究工作:
     (1)基于网状结构的改模知识本体建立和基于SWRL (Semantic Web Rule Language)语言的改模方案表达
     和现有只考虑利用概念间的上下层次关系描述经验性知识的模型不同,本文不仅考虑概念间的上下层次关系,同时也考虑概念间的复杂的自定义关系,提出了基于描述逻辑的改模经验性知识的表示方法,并借助本体开发工具建立了网状的改模知识本体,从而比较完整地描述了经验性知识概念之间存在的复杂语义关联关系。提出利用语义网络规则语言SWRL构建改模方案,SWRL语言能直接利用OWL文件中的概念(类)和概念之间的关系,实现对原始改模方案的结构化处理,从而解决了改模方案不被机器所理解的问题。
     (2)基于自定义关系语义相似度算法的构建和“语义坐标”的建立
     和现有基于层次模型计算语义相似度的方法不同,本文提出了在考虑层次关系和自定义关系的基础上进行语义相似度计算的方法,通过计算把所有概念分为若干大类,从每个大类中筛选出一系列“中心概念”,提出以这些“中心概念”形象地扮演概念的“语义表述坐标”,每条改模方案是各条坐标轴上的语义值向空间某点进行投影映射的结果。这样解决了由于概念的多样性引起每种特征属性的不同取值数量多,当针对这些属性取值进行规则归纳时,将导致大量应用有限的规则出现的问题。从而可以极大地减少所归纳知识中涉及的概念数量,提高规则的简洁程度和适用范围。
     (3)基于粗糙集的改模方案增量式更新
     在粗糙集基础上,作者提出了一种规则的增量式获取方法,并首次应用于注塑模改模知识发现。首先对原有的差别矩阵进行改进,设计了基于改进的差别矩阵求核与属性约简增量更新算法;然后针对求属性约简过程中进行析取与合取运算的时候计算量大的问题,引入了分明差别矩阵,简化了属性约简的计算复杂度;最后通过计算规则的精度和覆盖度,并通过设定规则的阈值,对规则进行提取,得到了完备的改模规则集,提高了系统学习的效率
     (4)增量式更新的改模知识管理系统设计与实现
     设计了一个增量式改模知识管理系统,实现了改模知识领域本体语义编辑、语义坐标维护、改模方案知识维护、改模方案聚类、基于增量式的改模方案更新等功能。
     通过上述研究工作,解决了如何表达改模方案特征属性值之间复杂的语义关系,如何获得典型改模方案,如何持续进行增量式更新等问题,为模具制造企业有效地发现改模方案中蕴含的改模知识提供相关理论和方·法。
The quality of the plastic parts was depended on the injection mould. The plastic parts with complicated molding surface and structure, most of them were customized, and mould tests of the injection moulds were hard to be successful with the general designing and manufacture principles, so the mould repairs were inevitable in practice. In the process of the mould repair, the mould repair engineer should formulate the reasonable mould repair scheme with the traditional mould designing knowledge, manufacturing knowledge and the experience knowledge which accumulated in the mould repair practicing. The experience knowledge was accumulated and improved by constant practicing and accepting lessons. So many mould enterprises began to collect related historical records which were documented in the form of "problems and solutions" to form electronic mould repair schemes" which was helpful to the engineers find the similar mould repair schemes by searching the similar problem when a new mould repair project was formulated. But with the increasing number of cases, more and more schemes can be referenced and many similar problems have different solutions, so the engineers will be puzzled in lots of cases. The only way to resolve the questions was that the inherent disciplines should be concluded from the history projects and the true " experience knowledge" asset will be obtained. There are three kinds of difficulty to obtain the experience knowledge. Firltly. there are complicated semantic relationships among the different values of the mould repair schemes attributes. How to establish the complex related model for different characteristic parameters of mould repair schemes and how to extract the new are very difficulty. Secondly, the mould repair experience knowledge was variety and uncertainty which led to that the inherent disciplines from the mould repair schemes will be concluded. Lastly, practical application demanded the mould knowledge management system possessed the compacity of inducing rules from the knowledge database constantly. The new mould repair case can affect the original data decision table, and the system should extract the new rules and induce new knowledge from the mould repair cases, so how to use the new mould repair information and establish the sustainable learning mechanism was another difficulty.
     According to the difficulty, the main studies of the paper had been conducted as following.
     (1) The establishment of the mould repair knowledge ontology based on the nets structure and the representation of the mould repair scheme based on SWRL (sematic web rule language)
     By contrast with the model that only using the hierarchy relations among the concepts discribing the experience knowledge, the new mould repair experience knowledge representation model based on description logic was built, and not only the hierarchy relations but also the complex user-defined relations among the concepts were took into account. The nets mould repair knowledge ontology was established. Then the complex semantic relationships among the concepts of the mould repair experience knowledge were described completely. The mould repair schemes were built by SWRL which can use directly the concepts (classes) and the relations between the concepts of OWL, which can carry out that the mould repair schemes can be structured. So the problem that the mould repair schemes can't be recognized by the computer was solved.
     (2) The establishment of the algorithm for the similar calculating between the concepts based on the ontology sematic nets model and the construction of the "sematie coordinate". By contrast with the method of similar calculating based on the hierarchy relationships model, a new semantic similarity computation algorithm based on the ontology semantic nets model was built, in which the user-defined relationships among the concepts and the subclass relationships were both involved. A series of "center concepts" which were used to act the "semantic expression coordinate" of the concepts were obtained by the new algorithm. The mould repair scheme was the mapping point from every concept semantic value. It can resolve the problem that lots of rules with limit application were generated when the many different values were concluded. So the number of the concepts for rule induction was reduced by using the method, and the concise degree and the application ranges of the rules were improved.
     (3) The mould repair scheme incremental updating algorithm based on the rough set
     A rules incremental updating algorithm based on rough set was built and applied to the repairing knowledge discovery of injection mould.. Firstly, the discernibility matrix was improved, and the incremental updating algorithm for finding the core and attribution reduction based on discernibility matrix were built. Secondly, according to the heavy computation of disjunction and conjunction in the process of attribution reduction, the distinct difference matrix was adopted which can reduced the calculation complexity. Lastly, the complete mould repair rule set was obtained by calculating the rule's accuracy and the coverage, setting the threshold value, and inducing the rules.
     (4)The design and implementation of the mould repairing knowledge management system based on incremental updating
     The system was designed to carry out the functions such as semantic editing of the mould repair knowledge domain ontology, maintenance of the semantic coordinate and mould repair schemes knowledge, clustering of the mould repair schemes, incremental updating of the mould repair schemes.
     Through the above research work, the problems how to express the complex sementic relations among the values of mould repair schemes attributes, how to obtain the typical mould repair scheme, and how to incremental update constantly were solved. The methods and conclusions drawn from the paper will provide an efficient way to discover mould repair knowledge in mould manufacture enterprises.
引文
[1]阮雪榆,李志刚,武兵书,等.中国模具工业和技术的发展[J].模具技术,2001,(2):72-74
    [2]周永泰.“十一五”振兴模具工业的途径与对策[J].机械工人冷加工,2005,7:11-15
    [3]李和平,吴霞.现代模具行业现状和发展趋势综述[J].商场现代化,2007,(1):257
    [4]洪丽华,陈永禄.中国模具工业现状与模具技术发展趋势[J].机电技术,2007,2:96-99
    [5]周永泰.中国模具工业的现状与发展[J].航空制造技术,2007,12:37-39.
    [6]屈伟平。我国模具制造业发展现状、存在的问题及对策[J]。模具技术,2006,(5):59-63.
    [7]肖五木.改模工艺规划系统的研究与设计[D].广州:广东工业大学,2008
    [8]田文生,许建新等.模具企业知识管理[J].电加工与模具,2003,(1):4-5
    [9]陈晨.注塑模改模知识发现研究[D].广州:广东工业大学,2009
    [10]孙丽.工艺知识管理及其若干关键技术研究[D].大连:大连交通大学,2004
    [11]史忠植.知识发现[M].北京:清华大学出版社,2002
    [12]Fayyad U, Gregory P.S., Padhraicn S. From Data Mining to Knowledge Discovery in Databases. AAAI'97[C].1997,37-54
    [13]Fayyad U, Paul S. Data mining and KDD:Promise and challenges [J]. Future Generation Computer Systems.1997,13:99-115
    [14]王清毅,陈恩红,蔡庆生.知识发现的若干问题及应用研究[J].计算机科学,1997.5:73-77
    [15]Goebel M, Gruenwald L. A survey of data mining and knowledge Discovery software tools. ACM SIGKDD[C],1999,1(1):20-33
    [16]Zhi Hua Zhou. Three perspectives of Data Mining [J]. Artificial Intelligence. 2003,143:139-146
    [17]吉根林,帅克孙志辉.数据挖掘技术及其应用[J],南京师范大学学报(自然科学版).2000,23(2):25-27
    [18]Katharina Morik. Knowledge Discovery in Databases--An inductive logic programming approach[J]. Lecture Notes in Computer Science.1997.1337: 429-437
    [19]李雄飞,李军.数据挖掘与知识发现.北京:高等教育出版社[M].2003
    [20]Hideyuki Maki. Yuko Teranishi. Development of automated data mining System for quality control in Manufacturing [J]. Lecture Notes in Computer Science.2001.2114:93-100
    [21]Yung sheng Lian. Data Mining for Evolutionary design optimization[A],34th AIAA fluid dynamics conference and exhibit. Portland, Oregon.2004
    [22]Markus Thannhuber. An autopoietic approach for building Knowledge Management Systems in Manufacturing enterprises. Annals of the CIRP 2001. 50(1):313-318
    [23]Jennifer Rowley. Knowledge organization in web-based environment. Management Decision.2001,39(5):355-361
    [24]尚福华,李军,王梅,等.人工智能及其应用[M].北京:石油工业出版社.2005
    [25]邓志鸿,唐世渭,张铭,等Ontology研究综述[J].北京大学学报(自然科学版),2002,38(5):730-738.
    [26]M R Genesereth, N J Nilsson. Logical Foundations of Artificial Intelligence [M]. San Mateo:Morgan Kaufmann Publishers,1987.
    [27]Neches R. Fikes R E. Gruber T R etc. Enabling Technology for Knowledge Sharing[J]. AI Magazine,1991,12(3):36-P56.
    [28]Gruber T. Ontolingua, A Translation Approach to Portable Ontology Specifications[J]. Knowledge Acquisition,1993, Vo5:199-200
    [29]Studer R, Benjamins V R, Fense ID etc. Knowledge Engineering, Principles and Methods[J]. Data and Knowledge Engineering,998,25(12): 161-197.
    [30]Perez A G、Benjamins V R. Overview of Knowledge Sharing and Reuse Components:Ontologies and Problem Solving Methods [C]. Proceedings of the IJCAI-99, workshop on Ontologies and Problem Solving Methods(KRR5),1999.1-15.
    [31]刘同明,等.数据挖掘技术及其应用[M].北京:国防工业出版社,2001
    [32]毛国君.数据挖掘原理与算法[M].北京:清华大学出版社,2005
    [33]史小松.数据挖掘技术中聚类的几种常用方法比较[J].中国科技信息,2009年第12期,99-101
    [34]牟廉明.数据挖掘中聚类方法比较研究[J].内江师范学院学报,2003,18(2) 16-20
    [35]王利,张喜平,郭林.增量式知识获取算法综述[J].重庆邮电大学学报(自然科学版),2007,6:99-102
    [36]Chan, C. C., A rough set approach to attribute generalization in data mining[J]. Information Science,1998,107:169-176.
    [37]刘宗田。属性最小约简的增量式算法[J]。电子学报,1999,27(11):96-98.
    [38]陈云化,叶东毅。基于粗糙集理论的一个增量算法[J]。计算机科学,2002,29(9)(专刊):53-55.
    [39]李银花,张继福,高素芳。基于粗糙逻辑的增量式属性约简算法[J]。系统仿真学报,2005,17(2):313-315.
    [40]白俊卿,刘琼荪。一种增量式属性约简算法[J]。计算机工程与应用,2006,24:174-175.
    [41]Chan, C. C., Incremental learning of production rules from examples under uncertainty:a rough set approach[J]. International Journal of Software Engineering and Knowledge Engineering,1991,1(4):439-461.
    [42]Shan, N. and Ziarko, W. An incremental learning algorithm for constructing decision rules[A]. In:Kluwer, K.S. Ed. Rough Sets, Fuzzy Sets and Knowledge Discovery[C].1993,326-334. Springer-Verlag, London, U.K. ISBN:3-540-19885-7
    [43]Shan, N. and Ziarko, W. Data-based acquisition and incremental modification of classification rules[J].Computational Intelligence,1995, 11(2):357-370.
    [44]安利平,吴育华,仝凌云。一种基于粗糙集理论的规则获取算法[J]。管理科学学报,2001,4(5):74-78.
    [45]Tsumoto, S. and Tanaka, H. Primerose:probabilistic rule induction method based on rough sets and resampling method[J]. Computational Intelligence. 1995,11(2):389-405.
    [46]安利平,吴育华,仝凌云。增量式获取规则的粗糙集方法[J]。南开大学学报(自然科学版),2003,36(2):98-103.
    [47]Bang, W. and Bien, Z.. New incremental inductive learning algorithm in the framework of rough set theory[J]. International Journal of Fuzzy Systems, 1999,1(1):25-36.
    [48]胡建龙,岳晓冬,李德玉。一种新的规则获取增量式算法[J]。山西大学学报(自然科学版),2006,29(2):139-141.
    [49]Zheng, Z. and Wang, G. RRIA:A rough set and rule tree based incremental knowledge acquisition algorithm[J]. Fundamenta Informaticae,2004,59: 299-313.
    [50]王杨,闫德勤,张风梅。基于粗糙集和决策树的增量式规则约简算法[J]。计算机工程与应用,2007,43(1):170-172
    [5]]李莉。基于可变精度模型的增量式归纳学习[J]。计算机科学,1999,26(1):55-58.
    [52]李雪兰,徐从惠,耿卫东。基于改进差别矩阵的增量式规则提取算法[J]。计算机科学,2003,30(5月专刊):46-49.
    [53]王利,王国胤,吴渝。基于可变精度粗集模型的增量式规则获取算法[J]。重庆邮电学院学报(自然科学版),2005,17(6):709-713.
    [54]左敏,郭成城,晏蒲柳。一种新的基于粗集的增量式规则提取算法[J]。武汉大学学报(理学版),2002,(3):
    [55]戚湧,於东军,杨静宇,刘风玉。基于IREA的模糊神经网络及其在网络拥塞预测中的应用[J]。系统仿真学报,2004,16(5):1005-1008.
    [56]何明,冯博琴,马兆丰,傅向华。基于增量式遗传算法的粗糙集分类规则挖掘[J]。西安交通大学学报,2004,38(6):579-582.
    [57]王秀,叶东毅。基于分布约简的获取规则的增量式方法[J]。福州大学学 报(自然科学版),2005,33(1):16-19.
    [58]郭森,王知衍,吴志成,严和平。基于粗糙集理论的增量式规则获取[J]。计算机应用,2005,25(11):2621-2623.
    [59]黄治国,王加阳,罗安。一种基于Rough集获取规则知识的增量式学习方法[J]。计算机工程与应用,2006,35(1):163-165.
    [60]Ziarko, W., Peters, J. F. and Skowron, A., Incremental learning and evaluation of structures of rough decision tables [A], In:Transactions on rough sets Ⅳ[W],2005, Lecture notes in computer science, Vol.3700,162-177, Springer, Berlin. ISSN 0302-9743
    [61]年志刚,梁式,麻芳兰,李尚平.知识表示方法研究与应用[J].计算机应用研究,2007,5:4-5.
    [62]尚福华,李军,王梅,等.人工智能及其应用[M].北京:石油工业出版社,2005.
    [63]赵震,吕士军,彭颖红,等.冲裁模具结构设计知识表示与处理技术研究[J].中国机械工程,2003,14(4):299-301.
    [64]王豪行,阮雪榆,彭颖红,等.冲模结构知识库知识与控制性知识的表示[J].模具技术,1999.6:299-301
    [65]张月蓉.注塑模具知识库系统中知识表示的研究[J].模具技术2006.5:3-6.
    [66]张为民,李爱平.模具设计案例知识管理系统的研究与开发[J].计算机工程,2005.31(6):197-199
    [67]熊平原,毛宁,陈庆新等,基于压铸模领域本体构建研究[J].模具工业,2009.7,5-10.
    [68]杜松晏,毛宁,陈庆新.基于OWL语言的注塑模改模知识表示方法.[J].中国制造业信息化.,2010,39(9):11-16.
    [69]梁志伟,毛宁,陈庆新.轮胎模具工艺知识本体描述方法研究.[J].模具工业,2010,36(7):8-12.
    [70]沈卫华,毛宁等。基于本体的模具设计知识表达、检索与推理[J]。机械科学与技术,2005,24(11):1301-1305.
    [71]李英杰,陈新度等。基于本体的模具设计知识管理系统的研究[J]。锻压 技术,2008.6,121-125.
    [72]胡沙,杨双荣,李建军。基于经验反馈模型的模具企业知识获取框架[J]。计算机应用,2009.5,1457-1460.
    [73]柯旭贵,张佑生.基于实例推理的冲压模结构设计的框架知识表示[J].计算机工程与应用,2002.38(13):254-256
    [74]刘坚,许楚銮,于德介等。基于本体的监测组态知识表示研究[J]。湖南大学学报(自然科学版),2009.5,
    [75]Farhad Ameri, Joshua D. Summers. An Ontology for Representation of Fixture Design Knowledge [J], Computer-Aided Design and Applications, 2008.5,601-611.
    [76]Guangming Wang. Distributed Ontology-Based Knowledge Management Framework [A], Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007),2007.5. Haikou. P. R. China.
    [77]Kuo-Cheng Ku, Anthony Wensley, Hsing-Pei Kao. Ontology-based knowledge management for joint venture projects [J], Expert Systemswith Applications,2008(35),187-197.
    [78]Valerio Cisternino, Eliana Campi. Angelo Corallo, Nouha Taif. Ontology-based knowledge management systems for the new product development acceleration:Case of a community of designers of automotives. [A],2008 IEEE International Conference on Signal Image Technology and Internet Based Systems. Washington D. C., USA,672-677.
    [79]Jae-Hyun Lee and Hyo-Won Suh. Ontology-based Multi-layered Knowledge Framework for Product Lifecycle Management [J], Concurrent Engineering 2008(16).301-311.
    [80]Amandeep S. Sidhu, Tharam S. Dillon, Elizabeth Chang. Ontology-based Knowledge Representation for Protein Data [J],2005 3rd IEEE International Conference on Industrial Informatics,10-12 August 2005. Perth, Australia.
    [81]Huang Guo, Zhou Zhu-rong, Research on domain ontology based concept semantic similarity computation, Computer Engineering and Design. Editorial Department of Computer Engineering and Design,2007, pp.2460-2463.
    [82]Chira, C.:An agent-based approach to knowledge management in distributed design, Journal of Intelligent Manufacturing,17,2006,737-750.
    [83]Kulvatunyou, B; Ivezic, N; Wysk, R. A.; Jones, A.:Integrated product and process data for business to business collaboration, Artificial Intelligence for Engineering Design, Analysis and Manufacturing,17,2003,253-270.
    [84]Pehlivan, S.; Summers, J. D.:A review of computer-aided fixture design with respect to information support requirements, International Journal of Production Research,2006,1-19.
    [85]于同敏,查志锋,李俊龙等。基于模糊神经网络的注塑制品缺陷分析诊断[J]。模具制造,2004,(5):13-15。
    [86]郁滨,钟汉如.智能消除注塑制品缺陷的研究[J].中国机械工程,2001,12(6):624-628
    [87]金新明,朱学峰.一种用于注塑过程参数设定的智能方法[J].中国机械工程,2001,12(10):1162-1164
    [88]王德翔,刘来英,王振宝,等.基于人工神经网络技术的注塑成型工艺参数优化[J].模具技术,20016:1-4
    [89]张志鸣。塑料模具加工制造中的设计修正与反馈[J]。塑料,2000,(1):24-25。
    [90]杨海鹏。塑料模的试模与维修[J]。模具制造,2002,(1):31-33。
    [91]吴山洪,成艾国。汽车覆盖件成型模试模调整时R角不良问题处理[J]。机电产品开发与创新,2004,(7):51-53。
    [92]张晓陆。浅谈试模管理与程序[J]。机械与模具,2006,(7):70-72
    [93]王毅,刘江涛,陈庆新,毛宁。失效模式及后果分析在注射模设计中的应用[J]。模具工业,2006,(9):10-12
    [94]王义林,郑金桥,李志刚。基于KBE的大型复杂冲压件工艺信息模型的研究[J]。华中科技大学学报(自然科学版),2006,34(1):4-7.
    [95]姜开宇,孙传亭,于同敏。注塑模具设计知识的表达与获取[J]。大连轻工业学院学报,2003,22(4):281-283.
    [96]陈军,石晓祥,杨红波,等.知识挖掘方法在板料冲压工艺设计中的应用[J].上海交通大学学报,2003.37(5):675-678
    [97]张雷,袁楚明,陈幼平等。模具曲面抛光工艺知识的获取[J]。中国机械工程,2001,12(4):424-426。
    [98]王辉,汪翰.周雄辉.等.注塑模CBR设计决策系统中利用神经网络方法进行事例检索的研究[J],机械科学与技术,2003.22(3):474-479
    [99]尹纪龙.罗应兵,李大永,等.圆筒件拉深成形仿真结果的工艺知识挖掘[J].上海交通大学学报,2004.38(7):1065-1068
    [100]王迎春,尹纪龙,李大永.等.基于决策树算法C4.5的冲压工艺知识发现[J].机械科学与技术,2004.32(12):1506-1508
    [101]Zhou C, Ruan F. Applying rough sets to discover tacit knowledge from stamping die designs J]. Journal of plasticity engineering. 2007,14(2):130-136
    [102]石峰,娄臻亮,陆金桂,等.基于模糊粗糙集模型的注塑模浇注系统方案智能化设计研究[J].机械工程学报,2003,38(9):123-127
    [103]谢英星,陈晨,毛宁,等.模具改模信息管理系统设计与开发[J].制造技术与机床.2007(12).23-28.
    [104]Chen Chen, Mao Ning, Chen Qing-xin. Knowledge representation and induction for injection mould repairings based on hierarchy model. ICFDM2008.2008.9:900-904.
    [105]Chen Chen, Mao Ning, Chen Qingxin. Knowledge induction for injection mould repairings based on rough set. FSKD2008,2008.8:166-171.
    [106]陈晨,毛宁,陈庆新.注塑模改模知识发现方法研究.机械科学与技术.2009.28(3):311-316
    [107]Yi Wang. A new method for Process Plan reasoning in injection mould repairings [J]. Advanced Materials Research.139-141(2010):1044-1047.
    [108]Wang yi, Chen Qingxin Mao Ning. A new method for knowledge inducing in the injection mould repairing. ICFDM2010,2010.7:562-565.
    [109]王毅,陈庆新.毛宁.基于自组织模糊规则归纳的注塑模改模知识发现[J].工程设计学报。2010.17(3):168-17
    [110]Niu Qiang, Xia Shixiong, Tan Guojun. An improved incremental updating algorithm for the core computation of decision table[C]. International Conference on Artificial Intelligence and Computational Intelligence 2009:58-62.
    111]牛强,语义环境下的矿井提升机故障诊断研究[D],中国矿业大学.2010。
    112]王钊,车辆导航电子地图的自增量更新[D],清华大学.2012。
    113]杨兆升,汪健,李丽丽.导航电子地图增量更新方法研究[J].交通信息与安全,2009,27(2):10-14.
    114]张庆,基于激光测量的板材矫直机智能控制系统的研究[D],浙江大学.2012。
    115]余旭,刘继红,何苗.基于领域本体的复杂产品设计知识检索技术[J].计算机集成制造系统,2011,17(2):225-231.
    116]周扬,李青.飞机故障知识的本体建模及语义检索[J].计算机工程与应用,2011,47(8):12-15.
    117] Yuh-Jen Chen, Development of a method for ontology-based empirical knowledge representation and reasoning [J], Decision Support Systems 50(2010) 1-20.
    118] T Guo, D G Schwartz, F Burstein, etal. Codifying collaborative knowledge using Wikipedia as a basis for automated ontology learning [J], Knowledge Management Research & Practice 7 (2009) 206-217.
    119] B Kamsu Foguem, T Coudert, C Beler, etal. Knowledge formalization in experience feedback processes:An ontology-based approach [J], Computers in Industry 59 (7) (2008) 694-710.
    120] J H Bradley, R Paul, E Seeman. Analyzing the structure of expert knowledge[J], Information & Management 43 (1) (2006) 77-91.
    121] S Schulz, U Hahn. Part-whole representation and reasoning in formal biomedical ontologies [J], Artificial Intelligence in Medicine 34 (3) (2005) 179-200.
    122] W L Xu, L Kuhnert, K Foster, etal. Object-oriented knowledge representation and discovery of human chewing behaviors [J], Engineering Applications of Artificial Intelligence 20 (7) (2007) 1000-1012.
    123] Franz Baader etal. The Description Logic Handbook:Theory, Implementation and Applications [M]. Cambridge University Press,2003.
    [124]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.
    [125]石莲,孙吉贵.描述逻辑综述[J].计算机科学,2006,33(1):194-197.
    [126]蒋运承,史忠植,汤庸,王驹.面向语义Web语义表示的模糊描述逻辑.软件学报,2007,18(6):1257-1269.
    [127]史忠植,常亮.基于动态描述逻辑的语义Web服务推理.计算机学报,2008,31(9):1599-1611.
    [128]LANG J. Logic preference representation and combinatorial vote [J]. Annals of Mathematics and Artificial Intellgence,2004,42(1/3):37-71.
    [129]丘威,张立臣.本体语言综述研究[J].情报杂志,2006.7:61-64.
    [130]岳静,张自力.本体表示语言研究综述[J].计算机科学,200633(2):158-162.
    [131]王欢.基于本体和SWRL的空间关系推理的设计与实现[D].西安,陕西师范大学,2007.
    [132]关铭.以OWL DL及SWRL为基础建置推理原型系统——以大学排裸为例[D].郑州,中原大学.2004.
    [133]陈布伟,李冠宇,张俊等.基于语义网络规则语言的推理机制框架设计[J].计算机工程与设计,2010,31(4).
    [134]刘宇松.本体构建方法和开发工具研究[J].现代情报,2009.29(9):17-24.
    [135]Kumazawa T. Saito O, Kozaki K. etal. Toward knowledge structuring of sustainability science based ontology engineering [J]. Sustainability Science. 2009,4(1):99-11.
    [136]李景.本体理论在文献检索系统中的应用研究[M].北京图书馆出版社2005.
    [137]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.
    [138]牛强,夏士雄,谭国俊,等.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,2009,30(5):872-876.
    [139]刘书暖,张振明,田锡天,等.基于聚类分析法的典型工艺路线发现方法[J].计算机集成制造系统,2006,12(7),996-1000.
    [140]高伟,殷国富,成尔京.辅助性工艺知识库组成及知识获取方法[J].计算机集成制造系统,2004,10(7):843-847.
    [141]刘文军,游兴中.一种改进的凝聚层次聚类法[J].吉首大学学报(自然科学版),2011.7,32(4):11-14.
    [142]于娟,韩建民,郭腾芳等.基于聚类的高效k-匿名化算法[J].计算机研究及发展,2009,46(增刊):105-111.
    [143]刘紫玉,黄磊.基于领域本体模型的概念语义相似度计算研究[J].铁道学报,2011,33(1),52-57.
    [144]Leacock C, Chodorow M. Combining local context and WordNet Similarity for Word Sense Identification [C] FELBAUM C. WordNet:An Electronic Lexical Database. Cambridge:MIT Press,1998:265-283.
    [145]Tversky A. Features o f Similarity [J]. Psychological Review,1977,84(2): 327-352.
    [146]陈杰,蒋祖华.领域本体的概念相似度计算[J].计算机工程与应用,2006,42(33):163-166.
    [147]黄果,周竹荣.基于领域本体的概念语义相似度计算研究[J].计算机工程与设计,2007,28(5):2460-2463.
    [148]胡哲,郑诚.一种改进的基于领域本体的概念语义相似度算法[J].齐齐哈尔大学学报,2013,29(1).
    [149]周琳,基于本体的航天企业三维工艺指导知识表达及自组织方法研究[D]南京,南京理工大学,2013.
    [150]李严,基于语义相似度的关联词柔性群簇模型[D].上海,上海大学,2007.
    [151]王利,张喜平,郭林.增量式知识获取算法综述[J],重庆邮电大学学报(自然科学版),2007,6:99-102.
    [152]Pawlak, Z. Rough sets[J]. International Journal of Information and Computer Sciences,1982,11(5):341-356.
    [153]苗夺谦,李道国.粗糙集理论、算法与应用[M].北京:清华大学出版社2008.
    [154]安利平.基于粗集理论的多属性决策分析[M].北京:科学出版社,2008.
    [155]Jelonek J, et al. Rough set reduction of attributes and their domains for neural networks [J].Computational Intelligence,1995,11(2):338-347.
    [156]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.
    [157]王珏.粗糙集理论及其应用研究[D].西安,西安电子科技大学,2005.
    [158]刘少辉,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.
    []59]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.
    [160]汪小燕.一种改进的差别矩阵及其求核方法[J].安徽工业大学学报(自然科学版),2009,26(1):86-88.
    [161]聂红梅,周家庆.一个新的差别矩阵及其求核方法[J].四川大学学报(自然科学版),2007,44(2):277-283.
    [162]杨明,孙志挥.改进的差别矩阵及其求核方法[J].复旦学报(自然科学版),2004,43(5):865-868.
    [163]张振琳,黄明.改进的差别矩阵及其求核方法[J].大连交通大学学报,2008,29(4):79-82.
    [164]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.
    [165]Hu XiaoHua, Cercone N. Learning in relational databases:a rough set approach [J].Computational Intelligence,1995, 11(2):323-337.
    [166]张文修.粗糙集理论与方法[M].北京:科学出版社,2001.
    [167]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.
    [168]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.

© 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