用户名: 密码: 验证码:
基于案例推理的煤矿回采巷道支护决策系统研究
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
煤矿回采巷道支护参数的合理选择,决定着煤矿回采工作面的能否正常运转,也是保障煤矿安全生产的重要因素之一。由于煤矿回采巷道支护参数影响因素具有主观和客观上的不确定性和复杂性,有些参数值的获取也较为困难,采用理论计算、数值模拟、相似材料模拟等方法进行支护参数设计,结果并不尽理想。近年来随着计算机技术的快速发展,很多高校和科研院所把专家系统、决策支持系统引入到巷道支护设计应用中,取得了一定的成果,一定程度上提高了设计效率和效果,但由于传统的规则推理机制存在知识获取的“瓶颈”、规则库维护和更新困难、灵活性和适应性差等缺陷,制约着这些系统一直没能很好的推广应用。为此,本论文研究基于案例推理的煤矿回采巷道支护决策系统,克服传统的围岩分类和规则获取等困难,为煤矿回采巷道支护参数选择的智能决策探索新的方法,同时也拓展了案例推理技术理论在复杂案例推理中的应用。
     论文从探讨案例推理的基本原理入手,论述了案例的表示方法、案例检索策略、案例相似度的计算方法、案例属性权重的确定方法、案例的修正技术以及案例的学习和维护,并在此基础上结合煤矿回采巷道支护参数选择的特点,构建了基于案例推理的煤矿回采巷道支护决策系统模型。
     在收集大量回采巷道支护案例及其围岩参数的基础上,采用统计学分析方法,分析了煤矿回采巷道围岩的物理力学参数分布规律及各参数之间的相关关系。结果显示回采巷道围岩参数总体上表现出如下规律:(1)围岩的泊松比、密度和内摩擦角呈现较好的标准正态分布形态;(2)围岩的抗压强度与抗拉强度、内聚力和密度具有明显的正相关性,而与弹性模量,泊松比,内摩擦角的相关性较弱;(3)泥岩和粉砂岩的弹性模量随着埋藏深度的逐渐增加呈现出明显的增大趋势,而泊松比则呈现减小的趋势。并在此基础上针对回采巷道围岩参数缺失的具体情况,提出了基于岩石描述属性文本相似度和围岩参数相关性为基础的围岩参数取值算法,构建了煤矿回采巷道围岩参数数据库系统。
     最后采用面向对象编程语言和SQL Server2000数据库,设计开发了基于案例推理的煤矿回采巷道支护决策系统,实现了回采巷道支护参数智能选择、矿压监测、分析与预测及围岩参数取值等功能,并通过系统应用,在案例库不充分完备的情况下,取得了较好的效果。图表参
Rational selection of supporting parameters of coal mining roadway is one of important factors influencing the normal performing of coal mining workface, also is one of the important safeguards of safety in coal mine production. As influencing factors of choosing coal mining roadway supporting parameters are uncertain and complex, and some parameter values are more difficult to obtain, it is not satisfactory and effective to choose the support parameters by common methods such as theoretical calculation, numerical simulation, and similar material simulation. In recent years, with the rapid development of computer technology, many researchers of universities and institutes continuously try to apply expert system and decision support system to roadway support design and have acquired many achievements, which improve efficiency and effect. But with some shortcomings in traditional rule reasoning mechanism, such as bottleneck of knowledge acquisition, difficult in updating and maintenance of rule base, poor flexibility and adaptability and so on, these achievements have not been put into wide application.
     Therefore, this paper studies the coal mine roadway supporting decision-making system based on case-based reasoning, to overcome such difficulties as in traditional surrounding rock classification and rule acquisition, and to offer a new way to establish coal mining roadway support intelligent decision system. The study also expands the application fields of case-based reasoning technology.
     From the basic principle of case-based reasoning, this paper explores case representation, case retrieval, calculation method of case similarity, how to determinate the attribute weights of case, case modification technology, case learning and maintenance, based upon those explorations, combining the characteristics of coal mining roadway support parameter selection, the coal roadway supporting decision-making system based on case-based reasoning model has been established. On the basis of collecting numerous mining roadway support cases with parameters of their surrounding rock, employing the statistical analysis method, this paper analyzes the physical and distributional law of mechanical parameters of coal roadway surrounding rock and the relationship between the parameters.
     Results show general laws of the mining roadway surrounding rock parameters are as follows:(1) the poisson's ratio, density of the surrounding rock and the internal friction angle show a better standard normal distribution;(2) the compressive strength of surrounding rock has obvious positive correlations to tensile strength cohesion and density, but has weaker correlations to elastic modulus, poisson's ratio, internal friction angle;(3) elastic modulus of mudstone and siltstone increase with the gradual increase in buried depth, while the poisson's ratio shows the tendency of decreasing with the increase in buried depth. And according to the specific condition of lack of roadway surrounding rock parameters, the algorithm of parameter selection of surrounding rock is proposed based on similarity of rock feature description text and correlation of rock parameters, moreover, surrounding rock parameters database system for coal mining roadway is constructed.
     Finally, using object-oriented programming language and SQL Server2000database, coal mining roadway supporting decision system based on CBR has been developed, which can function properly in mining roadway support parameters selection, data analysis of mine ground pressure monitoring and so on; Further, despite the inadequate cases, the mining roadway support parameters selection engineering tested in the decision system still get the satisfactory results.
引文
[1]陈鸿,林丽闽,朱锋英等.层次相似算法模型及在管理案例检索的应用[J],系统工程学报,2003,18(1):31-32
    [2]Slade Stephen. Case-based reasoning:a research paradigm[J]. AI Magazine,1991,31 (1):42-55.
    [3]Yang H, Lu W F. Case adaptation in procase:a case-based process planning system for maching of rotational parts[J]. Artificial Intelligence for Engineering Design, Analysis and Manufacturing,1996,10(5):401-419.
    [4]林东豪.用基于案例的推理技术建立专家系统[J].计算机与现代化,1996(3):35-36.
    [5]Gavin Finnie, Zhaohao Sun.R5 model for Case-Based Reasoning[J]. Knowledge-Based Systems,2003(16):59-64.
    [6]David W. Patterson et al.Efficient Similarity Determination and Case Construction Techniques for Case-Based Reasoning[J]. ECCBR,2002:292-305.
    [7]徐明,胡守仁.论CBR研究中的若干误区[J].微电子学与计算机,1994,(5):28-30.
    [8]王日新.基于案例推理的智能诊断技术研究及应用[D].哈尔滨工业大学博士学位论文,2002:27-28.
    [9]L Benini,A Bogliolo, G DeMicheli. A survey of design techniques for system-level dynamic power management [J]. IEEE TRANSACTIONS ON VLSI SYSTEMS,2000,8 (3):299-316.
    [10]WeiserM, Welch B,DemersA, Shenker S. Scheduling for reduced CPU energy [A]. Proceedings of the First Symposium on Operating Systems Design and Implementation (OSD Ip94) [C]. Nov.1994:13-23.
    [11]Kinshuk Govil, Edwin Chan, HalWasserman. Comparing algorithm for dynamic speed-setting of a low-power CPU[A]. Proceedings of the 1st annual international conference on Mobile computing and networking [C]. NY USA:ACM Press,1995:13-25.
    [12]杨健,赵秦怡.基于案例的推理技术研究进展及应用[J].计算机工程与设计,2008,29(3):710-712.
    [13]何满潮,孙晓明著.中国煤矿软岩巷道工程支护设计与施工指南[M].北京:科学出版社,2004.
    [14]肖福坤等.煤矿巷道支护智能决策系统[J].辽宁工程技术大学学报,2004,23(3):293-295.
    [15]刘芳,姚莉,王长缨,等.基于语义Web的案例表示和CBR系统结构研究[J].计算机应用,2004,24(1):17-19.
    [16]Branting L K. Stratified Case-based Reasoning in Non-Refinable Abstraction Hierarchies[C]. Proceedings of the Second International Conference on Case-based Reasoning.Springer,1997:519-530.
    [17]Smsalamo M, Golobardes E. Rough Sets Reduction Techniques for Case-based reasoning[C]. Anon International Conference on Case-based Reasoning. Springer,2005:467-482.
    [18]陆建江,张亚非,苗壮,等.语义网原理与技术[M].北京:科学出版社,2007.
    [19]Chi R T,Whinston A B, Kiang M Y. Case-based Reasoning to Model Building[C].Proceedings of the 26th Hawaii International Conference on System Science,1993:324-332.
    [20]Lorcan Coyle,D6nal Doyle,Padraig Cunningham. Representing similarity for CBR in XML[C]. Advances in Case-Based Reasoning(Proceedings of ECCBR-04).Madrid,Spain:Springer,2004:119-127.
    [21]Yang S Y, Liao P C, Ho C S. An ontology-supported case-based technique for FAQ[C]. Taipei,Taiwan:Proc 17th International Conference on Software Engineering and Knowledge Engineering,2005:639-644.
    [22]杨振刚,邓飞其.CBR中案例相似性测度研究[J].计算机应用与软件,2008,25(6):222-223.
    [23]Lance GN.WilliamsW T. Computer programs for hierarchical polythetic classification[J]. Computer Journal,1966, (9):60-64.
    [24]Wilson D R,Martinez T R. Improved heterogeneous distance functions[J]. Journal of Artificial Intelligence Research,1997 (6):1-34.
    [25]SanchezMarreM, CortesU, Roda R I, Poch M L. Eixample distance:a new similaritymeasure for case retrieval [C].1st Catalan Conference on Artificial Intelligence, Tarragona, Catalonia. October 1998:246-253.
    [26]王玉,邢渊,阮雪榆.基于案例的推理循环中人工神经网络和遗传算法的四种模型[J].上海交通大学学报,2000,37(3):202-204.
    [27]沈奇.利用遗传算法进一步优化CBR案例推理模型[J].计算机与现代化.2013,2:147-149.
    [28]季赛,沈星,沈超.基于粗糙集和相似度量的CBR检索方法[J].计算机工程与应用,2006,-3:172-174.
    [29]陈建宏,郑海力,刘振肖,杨瑞波.基于优势关系的粗糙集的巷道支护方案评价体系[j].中南大学学报(自然科学版).2011,42(6):1698-1703.
    [30]龚锦红,凌仕勇.一种基于Rough集的案例推理模型的构建[J].东南交通大学学报,2012,29(2):42-45.
    [31]Dubois D, Esteva F, Garcia P, et al. Fuzzy Modeling of Case-based Reasoning and Decision[C].Proceedings of the Second International Conference case-based reason ing, LNAI 1266, Springer-Verlag,1997:599-610
    [32]Watson I, Marir F. Case-based Reasoning:A Review[J].The Knowledge Engineering Revirw,1994,9(4):327-354
    [33]Wikle W, Vollrath I, Althoff K D, et al. A Framework for Learning Adaptation Knowledge Based on Knowledge Light Approaches[C]. The 5th Gemran workshop on CBR,1997.
    [34]张贤坤.基于案例推理的应急决策方法研究[D].天津大学博士学位论文.2012
    [35]Grzymala-Busse J W. On the Unknown Attribute Values in Learning from Examples [J]. Lecture Notes in Computer Science,1991,542:368-377.
    [36]Hinrichs T R, Kolodner J L. The Roles of Adaptation in Case-based Design[C].Proceedings of case-based reasoning Workshop, Washington,1991:121-132.
    [37]Lee M. A Study of Automatic Learning Model of Adaptation Knowledge for Case-based Reasoning [J]. Information Sciences,2003,155:61-67.
    [38]Vong C M,Leung T P.Wong P K. Case-based Reasoning and Adaptation in Hydraulic Production Machine Design[J].Engineering Applications of Artificial Intelligence,2002,15: 567-585.
    [39]Grzymala-Busse J W, Wang A Y. Modified Algorithms LEM1 and LEM2 for Rule Induction from Data with Missing Attribute Values[C].Proceedings of the 5th International Workshop on Rough Sets and Soft Computing (RSSC'97) at the Third Joint Conference on Information Sciences(JCIS'97).USA, New Caledonia:Research Triangle Park,1997:69-72.
    [40]王辉,周雄辉,阮雪榆.基于遗传算法的事例改写策略在注塑成本评估CBR系统中的应用研究[J].中国机械工程,2002,13(22):1957-1960
    [41]常春光,崔建江,汪定伟等.案例推理中案例调整技术的研究[J].系统仿真学报2004,16(6):1260-1265
    [42]Fernandez Riverola F, Corchado J M. Employing TSK fuzzy models to automate the revision stage of a CBR system[C]. San Sebastian,Spain:10th Conference of the Spanish Association for Artificial Intelligence,2003:302-311.
    [43]张光前,邓贵仕.基于事例推理中差异驱动的事例修改策略研究[J].计算机应用,2005,25(7):1658-1660.
    [44]耿焕同,肖明军.聚类算法在范例库维护中的应用研究[J].计算机工程,2005,31(12):166-168.
    [45]Roger C. Schank. Dynamic Memory:A Theory of Reminding and Learning in Computers and People [M].Cambridge University Press New York, NY, USA,1983
    [46]Koton Phyllis. Medical Reasoning Program that Improves with Experience[C].Proceedings Annual Symposium on Computer Applications in Medical Care,1988:32-37.
    [47]Funk P, Xiong N. Case-based Reasoning and Knowledge Discovery in Medical Applications with Time Series[J].Computational Intelligence,2006,22(3/4):238-253.
    [48]Huang M J,Chen M Y,Lee S C.Integrating Data Mining with Case-based Reasoning for Chronic Diseases Prognosis and Diagnosis[J].Expert Systems with Applications,2007,32:856-867.
    [49]Park Y J,Choi E,Park S H. Two-step Filtering DataMining Method Integrating Case-based Reasoning and Rule Induction[J].Expert Systems with Applications,2009,36(l):861-871.
    [50]李一军,周浩.基于模糊类比推理的证券投资决策支持系统[J].决策与决策支持系统,1997,7,4:71-78
    [51]Noha J B,Leeb K C,Kim J K. A Case-based Reasoning Approach to CognitiveMap-driven Tacitknowledge Management [J].Expert Systems with Applications,2000,19:249-259.
    [52]Lee G H.Rule-based and Case-based Reasoning Approach for Internal Audit of Bank[J]. Expert Systems with Applications,2008,21:140-147.
    [53]Li H,Sun J,Sun B L.Financial Distress Prediction Dased on OR-CBR in the Principle of K-Nearest Neighbors[J].Expert Systems with Applications,2009,36(1):643-659.
    [54]Kolodner J L. Requirements for Natural Language Fact Retrieval[C]. Proceedings of the Annual Conference of the Association for Computing Machinery,1982:192-198.
    [55]赵卫东,李旗号,盛昭瀚.基于案例推理的决策问题求解研究[J].管理科学学报,2000,3(4):29-36.
    [56]Tseng Hwai-En, Chang Chien-chen, Chang Shu-Hsuan. Applying Case-based Reasoning for Product Configuration in Mass Customization Environments[J]. Expert Systems with Applications, 2005,29(4):912-925.
    [57]Yang S Y,Hsu C L. An Ontological Proxy Agent with Prediction,CBR and RBR Techniques for Fast Query Processing [J].Expert Systems with Applications,2009,36:9358-9370.
    [58]Tung Y H,Tseng S S,Weng J F,et al. A Rule-based CBR Approach for Expert Finding and Problem Diagnosis[J].Expert Systems with Applications,2010,37:2427-2438.
    [59]Hammond, Kristian J. Explaining and Repairing Plans that Fail [J]. Artificial Intelligence, 1990,45(1-2):173-228.
    [60]周涵.基于范例学习的内燃机油产品设计系统[D].北京:中国石油大学,1993.
    [61]Chen Lifan.Case-based reasoning expert system for concept design of economical car[C].Vehicle Electronics Conference,changchun,China,1999,1:360-364
    [62]Wang YP, Zhang QF,Liu HL, et al. The Expert System of Product Design based on CBR and GA[C]. International Conference on Computational Intelligence and Security Harbin. CHINA, 2007:144-147.
    [63]龚箭,张红,周敏.案例推理技术在包装设计系统中的应用[J].包装工程,2010,31(11):77-79.
    [64]Fdez-Riverola F,Corchado J M.CBR basel System for Forecasting Red Tides[J].Knowledge-Based Systems,2003,16:321-328.
    [65]汪季玉,王金桃.基于案例推理的应急决策支持系统研究[J].管理科学,2003,16(6):46-52.
    [66]Kim H K,Im K H,Park S C. DSS for Computer Security Incident Response Applying CBR and Collaborative Response[J].Expert Systems with Applications,2010,37:852-870.
    [67]袁晓芳,李红霞,田水承.煤矿重大瓦斯事故案例推理应急决策方法[J].辽宁工程技术大学学报(自然科学版).2012,31(5):595-599.
    [68]冯珍.煤矿事故应急救援案例推理系统研究[J].电子科技大学学报(社科版).2012,14(2):35-37.
    [69]沈新强,樊伟,韩士鑫,等.中心渔场智能预报系统的设计与实现[J].中国水产科学,2000,7(2):69-72.
    [70]叶施仁,史忠植.基于CBR的中心渔场预报[J].高技术通讯,2001.11(5):64-68.
    [71]杭小树,熊范伦.基于CBR的农作物病虫害预报专家系统[J].计算机工程与应用,2000,10:161-163.
    [72]唐晓敏,徐立鸿,恽源世.基于实例推理及其在农业病虫害诊断与防治中的应用研究[J].中 国农机化,2005,1:56-59.
    [73]高灵旺,陈继光,于新文,等.农业病虫害预报专家系统平台的开发[J].农业工程学院,2006,22(10):154-158.
    [74]李龙龙,赵惠燕.基于案例和模糊推理的农业虫害专家系统研究[J].计算机工程与设计,2007,28(22):5570-5572.
    [75]Riesbeck C K. Inside Case-based Reasoning [M]. Hillsdale, NJ, Lawrence Erlbaum Associates,1989.
    [76]Rissland E L, Daniels J J. Hybrid CBR-IR Approach to Legal Information Retrieval[C]. Proceedings of the International Conference on Artificial Intelligence and Law,1995:52-61.
    [77]胡小鹏.基于案例推理的刑事案件审判决策支持系统[D].上海海事大学硕士学位论文,2007
    [78]张清,田伟涛.隧道支护经验设计系统[A].见:中国岩石力学与工程学会编.中国岩石力学与工程学会第三次大会论文集[C].北京:中国科学技术出版社,1994,212-218
    [79]姚建国.WWW与基于事例推理(CBR)的岩土过程智能CAD[J].岩石力学与过程学报,2000,19(增):1053-1056
    [80]杨敏,肖珂,熊巨华.基于范例推理的桩基设计辅助系统[J].同济大学学报,2002,12(30):1439-1441
    [81]李娅.范例推理方法在道路岩石边坡稳定性评价中的应用[D].成都:西南交通大学硕士论文,2004,11
    [82]夏杨.基于范例的基坑支护方案选择决策支持系统[J].山西建筑,2009,35(27):106-107.
    [83]李梅.边坡案例推理稳定性评价系统及治理措施优化研究[D].武汉:武汉理工大学博士论文,2006:11-12
    [84]何忠明,曹平.基于模糊多属性决策模型的边坡支护设计方案优选[J].矿冶工程.2009,29(5):1-4.
    [85]曹立军,王兴贵等.融合案例与规则推理的故障预测专家系统[J].计算机工程,2006,32(1):208-210
    [86]杨辉,王永富,柴天佑.基于案例推理的稀土萃取分离过程优化设定控制[J].东北大学学报(自然科学版),2005,26(3):209-212.
    [87]王永军,彭学君.基于案例推理的企业商务智能研究[J].商业时代,2007(10):35-36.
    [88]刘景宁,刘涛,贺晓.基于CBR的故障诊断系统案例检索策略[J].华中科技大学学报(自然科学版),2008,36(3):17-19.
    [89]李伟峥,左勇志,霍达,刘育民,周乐云.基于案例推理的钢结构事故案例模糊检索与知识索引[J].工程抗震与加固改造.2012,34(1):31-36.
    [90]王日新.基于案例推理的智能诊断技术研究及应用[D].哈尔滨工业大学博士学位论文,2002:27-28
    [91]施鹏.面向区域成矿预测的案例推理方法研究[D].电子科技大学硕士论文,2011:10-11
    [92]周凯波,金斌,冯珊.一种分布式CBR工具研究与设计[J].华中科技大学学报:自然科学版,2005,33(9):33-35
    [93]Du Yunyan, Wen Wei, Cao Feng, Ji Min.A case-based reasoning approach for land use change prediction.Expert Systems with Applications.2010,37.5745-5750.
    [94]Xiaochun Luo, Geoffrey Qiping Shen, Shichao Fan.A case-based reasoning system for using functional performance specification in the briefing of building projects.Automation in Construction.2010,19.725-733.
    [95]Sungbin Cho,Hyojung Hong, Byoung-Chun Ha. A hybrid approach based on the combination of variable selection using decision trees and case-based reasoning using the Mahalanobis distance: For bankruptcy prediction.Expert Systems with Applications.2010,37:3482-3488.
    [96]Chin-Yuan Fana, Pei-Chann Changb, Jyun-Jie Linb, J.C, Hsiehb. A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification. Applied Soft Computing.2011,11(1):632-644
    [97]Gulfem Isiklar Alptekin, Gulcin Buyukozkan. An integrated case-based reasoning and MCDM system for Web based tourism destination planning. Expert Systems with Applications.2011,38(3):2125-2132.
    [98]Patterson D, Rooney N,Galushka M. SOPHIA-TCBR:A Knowledge Discovery Framework for Textual Case-based Reasoning[J].Knowledge-Based Systems,2008,21:404-414.
    [99]陈锐,李黔,尹虎.案例推理技术在钻井风险预测中的应用[J].断缺油气田,3013,20(2):225-227.
    [100]张培艳,吕恬生,宋立博.基于案例学习的排球机器人运动规划及其支持向量回归实现[J].上海交通大学学报,2006,40(3):461-465
    [101]张瑞新等.矿业专家系统研究进展,化工矿山技术,1992,21(5):55-58
    [102]柴建设.专家系统在矿业工程中的应用与发展[J].河北理工学院学报,1997,19(1):11-14
    [103]K. V. K. Prasad, et al. An Assessment of Expert System Building Tools for Miiing Applications[C],21th APCOM,1988
    [104]C. Marcelino, et al. A Mining Expert System for the Coal Industry[C],21th APCOM,1988
    [105]Signer S P, King R L. Evaluation of coal mine roof supports using artificial intelligence[C].23rd International Symposium on the Application of Computers and Operations Research in the Minerals Industries (APCOM), Arizona, USA.1992.
    [106]T.W. Camn, et al. An object-oriented Expert System for Underground Mining Meth god Select ion and Project Evaluation[C],23rd International Symposium on the Application of Computers and Operations Research in the Minerals Industries (APCOM), Arizona, USA.1992.
    [107]T.J.费希尔等.计算机化的矿山监控系统的进展—未来的展望,国外金属矿山,1986,2(3):50-55.
    [108]李英龙,矿山生产计划编制及采矿方法选择智能系统的研究[D]北京:北京科技大学 采矿系,1994
    [109]A.J.Basu,et al. Evaluation of a Prototype Expert System for Configuring Underground Coal Mines[C].23rd International Symposium on the Application of Computers and Operations Research in the Minerals Industries (APCOM), Arizona, USA.1992.
    [110]娄培杰.煤巷围岩稳定性分类及锚杆支护辅助设计系统研究[D],山东科技大学硕士学位论文,2008
    [111]李效甫,姚建国.回采巷道支护形式与参数合理选择的专家系统[R]煤炭科学技术.1990,1:28-32
    [112]冯夏庭等.巷道工程专家系统研究.东北工学院学报,1993,14(1):1-5.
    [113]谭云亮,姜福兴,宋振骐.顺槽巷道锚杆支护决策咨询系统研究[J].山东矿业学院学报.1998,17(1):33-38.
    [114]卫道毅,曹伍富.煤巷锚杆支护专家决策支持系统[J].淮南矿业学院学报.1998,18(3):40-42.
    [115]孟祥瑞,舒航.煤巷锚杆支护设计专家决策支持系统[J].煤矿自动化,1999,4:6-7.
    [116]李旭,卢宗华,王允,王新华,王光平,李兴东.巷道支护设计与施工决策支持系统的研究[J].1999,18(3):22-25.
    [117]蔡世明.基于人工神经网络的回采巷道围岩稳定性分类锚杆支护研究[D].重庆大学硕士学位论文.2002.
    [118]谢广祥,查文华.基于ANN-ES综放回采巷道锚杆支护设计[J].煤炭工程.2003,6:60-62.
    [119]肖福坤,孙豁然,刘晓军等.煤矿巷道支护智能决策系统[J].辽宁工程技术大学学报.2004,23(3):293-295.
    [120]肖福坤.巷道掘进支护计算机辅助决策系统研究[J].中国矿业.2007,16(2):80-82.
    [121]王文杰.岱庄生建煤矿巷道支护决策系统研究[D].山东科技大学硕士学位论文,2005,6.
    [122]王果,宋选民.缓倾斜煤层巷道围岩稳定性分类与锚杆支护设计决策系统研究[J].山西煤炭.2006,26(1):13-16.
    [123]王果.回采巷道围岩稳定性分类及锚杆支护设计决策系统研制与应用[D].太原理工大学硕士论文.2006.
    [124]李春睿.基于VC++6.0技术的回采巷道支护专家系统(TSES)的研究[D].辽宁工程技术大学学位论文.2006.
    [125]张连成,刘强凯,董长吉.基于Web的巷道支护决策系统的研究[J].煤炭技术.2007,26(10):57-59.
    [126]贾金河.煤巷锚杆支护设计与监测软件的开发及应用研究[J].煤矿开采,2004,9(1):62-64
    [127]张艺耘,孙昊,徐博.煤炭开采决策系统在宁东矿区的应用[J].西北煤炭.2007,5(4):21-23.
    [128]张文泉,马洪涛,李见波,张运海.可视化煤矿巷道支护决策专家系统的研制[J].山东科技 大学学报自然科学版.2007,26(4):27-30.
    [129]阮永芬,叶燎原.用灰色系统理论与方法确定深基坑支护方案[J],岩石力学与工程学报.2003,22(7):1203-1206.
    [130]乔春生,魏莉萍.岩石地下工程锚喷支护设计的人工智能方法及其集成[J].岩石力学与工程学报.2004,23(5):781-785.
    [131]徐杨青.深基坑工程决策与优化设计智能系统研究[J].资源环境与工程.2006,20:667-671.
    [132]张士科.史山矿回采巷道锚杆支护参数优化研究[D].河南理工大学硕士学位论文.2008.
    [133]陈国涛,孙丽敏,董山.基于模糊聚类分析的巷道分级支护研究[J].现代矿业.2012,517:21-25.
    [134]李伟山.煤矿回采巷道支护设计专家系统的探讨[J].河南科技.2012,(6):77
    [135]张新蛮.巷道支护专家系统软件的开发与实现[J].中国矿业.2012,21(3):100-102.
    [136]王悦等.基于案例推理专家系统中的案例表示方法[J].上海工程技术大学学报,2005.19(1):42-45
    [137]张光前等.基于案例推理及其应用前景[J].计算机工程与应用,2002,(20):52-54
    [138]王军.基于本体的房地产营销案例推理研究[D].武汉理工大学博士学位论文,2009
    [139]李玲娟,汤文宇等.基于XML的案例表示和案例库构造方法[J].计算机应用研究,2007,24(11):70-74
    [140]张月雷,左洪福.基于本体的案例表示和CBR系统结构研究[J].山东理工大学学报(自然科学版),2006,20(4):48-51
    [141]何绍华,李玲.案例检索以及案例库建设中的若干问题[J].情报科学,2003,21(6):629-631
    [142]胡官钦.案例推理的半导体生产规划优化方法[D].上海交通大学硕士学位论文,2009
    [143]贾兆红,唐俊,卢冰.原基于禁忌遗传算法的权重发现技术[J].计算机技术与发展,2006,16(11):26-31
    [144]王淑静.基于遗传禁忌算法的范例推理的研究[D].安徽大学硕士学位论文,2006
    [145]吴坚,梁昌勇,李文年.基于主观与客观集成的属性权重求解方法[J].系统工程与电子技术,2007,29(3):383-387
    [146]段军,戴居丰.案例修正方法研究[J].计算机工程,2006,32(6):2-3
    [147]李迎富.潘三矿深井动压回采巷道围岩稳定性分类及其支护设计[D].安徽理工大学硕士论文,2006

© 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