苹果病害事例库构建关键技术研究
摘要
苹果病害对苹果产量和品质都有较大的影响,我国在枰果病害的诊断与防治研究方面取得了大量的成果,也积累了丰富的诊断与防治经验,这些经验在生产管理中发挥了重要的作用。本文采用工智能中基于事例推理技术,以苹果病害诊断与防治工作的“经验”为研究对象,开展经验的事例化描述、检索与保存的关键技术研究。本文主要研究成果如下。
1.针对苹果病害事例的特点,提出了一种适合苹果病害事例描述的事例库、事例索引库的结构,形成了完整的事例库体系结构,并系统地梳理了能够完整描述苹果病的属性集合。所设计的具有3级结构的索引表既面向事例也面向属性；既体现了每个属性在各个事例中的分布细节,也反映了每个属性在整个事例库中的存在价值,具有系统性、层次性和灵活性的特点。
2.对索引表属性约简的关键技术进行了研究,提出了一种基于粗糙集理论和遗传算法的属性简约算法。该算法首先对取值为连续数值型的属性值进行了离散化处理,然后用粗糙集理论条件属性重要性度量方法来计算遗传算法中每代种群中各条染色体的适应度,最后用遗传算法实现属性约简。该算法充分考虑r属性在事例类别上的映射,能够借助遗传优化机制取得满意的约简属性子集。
3.对苹果病害事例检索的关键技术进行了研究,提出了基于知识的事例检索方法。该方法首先利用索引词汇农和1级索引表对用户的查询进行了特征提取,其次根据特征的数量对事例库中各事例索引表中的异构属性进行同构化处理,然后采用基于致病因素知识的模板检索和归纳检索相结合的策略建立决策树,最后利用决策树实现事例初步匹配。这种方法提升了用户查询请求的典型特征对查询请求所发生背景的代表能力,缩小了事例检索的范围,提高了检索的效率和准确度。
4.对苹果病害事例库维护和存储关键技术进行了研究,提出了一种基于事例推理性能的事例库更新机制和基于XML技术的存储结构。该机制将苹果病害诊断结果中的致病因素和病害类型作为判断事例推理性能的主要依据,通过比较新旧两个事例的推理性能,在增加和删除一个事例时对索引表进行调整。另外,针对农业生产特性和现代网络环境苹果病害事例库存储的要求,提出了一种基于XML技术的表达能力强、一致性好、安全性高的事例库存储结构。
Apple's disease has a greater impact on apple yield and quality. China's agriculturaltechnology sector has made great progress in the application of disease diagnosisand prevention of theoretical and applied researches. A wealth of experience playsan important role in the application of disease diagnosis and prevention. However,subject to the cultural level of the majority of farmers and the development ofagricultural economy, dissemination, communication and use of these experiencesare greatly restricted. Along with the rapid development of information technology,adopting appropriate artificial intelligence techniques to describe, store and useexpert’s experience, it is of great significance to improve apple disease diagnosisaccuracy and control effect and improve the apple output and quality.
Case-based reasoning technology is the only one in all artificial intelligencetechnology which is based on experience. This paper mainly researches the keytechnologies about ways to describe, retrieve and preserve the experience in theform of case. The study includes a total of four parts. Firstly, the paper designs thestructure of case base management system, the case base, the case and the indextable, and combes a set of attributes that describe apple’s diseases. Secondly, thepaper researches how to get a subset of attributes from the complete attribute setto construct the case base index table. Thirdly, the paper researches how to retrievea case which is the most similar to the background of the new apple disease problemfrom the case base, the solution of the retrieved case can offer the reference for thenew disease problem. Fourthly, the paper researches how to convert a diseaseproblem to a case, and the update mechanism of the case base when a new casejoins in it, meanwhile, the paper researches the storage structure of the case basebased on XML technology. Innovations in this paper are as follows:
1.The research on the key technology on the design of case base structure, astructure suitable for apple's disease cases description is given. In this paper, thestructure of case base management system, the case base, the case and the indextable are designed, and a complete set of attributes that describe examples of applediseases is combed. The index table is the core of the case base, and this paperdesigns3-grades index table structure based on the features of the apple diseasediagnosis and prevention work, in which the cases have different sets of attributes.The attribute-distribution-value in Grade1index table is used to describe thelocation of each attributes in the cases. The index structure is case-oriented andattribute-oriented，reflects the details of the distribution of each attribute in the casebase and the whole value of each attribute for the case base. The index structure issystematic, hierarchical and flexible.
2. The research on the key technology on attributes reduction in the index table,a algorithm used for attribute reduction based on rough set theory and genetic
results as the main basis to determine the case reasoning performance, and adjusts the index table when a case is added or deleted from the case base after comparing the old and new two cases'reasoning performance. In addition, a case base storage structure which is suitable for agricultural production characteristics and modern network environment, and has strong high security presentation, good consistency and high security is proposed.
1.针对苹果病害事例的特点,提出了一种适合苹果病害事例描述的事例库、事例索引库的结构,形成了完整的事例库体系结构,并系统地梳理了能够完整描述苹果病的属性集合。所设计的具有3级结构的索引表既面向事例也面向属性；既体现了每个属性在各个事例中的分布细节,也反映了每个属性在整个事例库中的存在价值,具有系统性、层次性和灵活性的特点。
2.对索引表属性约简的关键技术进行了研究,提出了一种基于粗糙集理论和遗传算法的属性简约算法。该算法首先对取值为连续数值型的属性值进行了离散化处理,然后用粗糙集理论条件属性重要性度量方法来计算遗传算法中每代种群中各条染色体的适应度,最后用遗传算法实现属性约简。该算法充分考虑r属性在事例类别上的映射,能够借助遗传优化机制取得满意的约简属性子集。
3.对苹果病害事例检索的关键技术进行了研究,提出了基于知识的事例检索方法。该方法首先利用索引词汇农和1级索引表对用户的查询进行了特征提取,其次根据特征的数量对事例库中各事例索引表中的异构属性进行同构化处理,然后采用基于致病因素知识的模板检索和归纳检索相结合的策略建立决策树,最后利用决策树实现事例初步匹配。这种方法提升了用户查询请求的典型特征对查询请求所发生背景的代表能力,缩小了事例检索的范围,提高了检索的效率和准确度。
4.对苹果病害事例库维护和存储关键技术进行了研究,提出了一种基于事例推理性能的事例库更新机制和基于XML技术的存储结构。该机制将苹果病害诊断结果中的致病因素和病害类型作为判断事例推理性能的主要依据,通过比较新旧两个事例的推理性能,在增加和删除一个事例时对索引表进行调整。另外,针对农业生产特性和现代网络环境苹果病害事例库存储的要求,提出了一种基于XML技术的表达能力强、一致性好、安全性高的事例库存储结构。
Apple's disease has a greater impact on apple yield and quality. China's agriculturaltechnology sector has made great progress in the application of disease diagnosisand prevention of theoretical and applied researches. A wealth of experience playsan important role in the application of disease diagnosis and prevention. However,subject to the cultural level of the majority of farmers and the development ofagricultural economy, dissemination, communication and use of these experiencesare greatly restricted. Along with the rapid development of information technology,adopting appropriate artificial intelligence techniques to describe, store and useexpert’s experience, it is of great significance to improve apple disease diagnosisaccuracy and control effect and improve the apple output and quality.
Case-based reasoning technology is the only one in all artificial intelligencetechnology which is based on experience. This paper mainly researches the keytechnologies about ways to describe, retrieve and preserve the experience in theform of case. The study includes a total of four parts. Firstly, the paper designs thestructure of case base management system, the case base, the case and the indextable, and combes a set of attributes that describe apple’s diseases. Secondly, thepaper researches how to get a subset of attributes from the complete attribute setto construct the case base index table. Thirdly, the paper researches how to retrievea case which is the most similar to the background of the new apple disease problemfrom the case base, the solution of the retrieved case can offer the reference for thenew disease problem. Fourthly, the paper researches how to convert a diseaseproblem to a case, and the update mechanism of the case base when a new casejoins in it, meanwhile, the paper researches the storage structure of the case basebased on XML technology. Innovations in this paper are as follows:
1.The research on the key technology on the design of case base structure, astructure suitable for apple's disease cases description is given. In this paper, thestructure of case base management system, the case base, the case and the indextable are designed, and a complete set of attributes that describe examples of applediseases is combed. The index table is the core of the case base, and this paperdesigns3-grades index table structure based on the features of the apple diseasediagnosis and prevention work, in which the cases have different sets of attributes.The attribute-distribution-value in Grade1index table is used to describe thelocation of each attributes in the cases. The index structure is case-oriented andattribute-oriented，reflects the details of the distribution of each attribute in the casebase and the whole value of each attribute for the case base. The index structure issystematic, hierarchical and flexible.
2. The research on the key technology on attributes reduction in the index table,a algorithm used for attribute reduction based on rough set theory and genetic
results as the main basis to determine the case reasoning performance, and adjusts the index table when a case is added or deleted from the case base after comparing the old and new two cases'reasoning performance. In addition, a case base storage structure which is suitable for agricultural production characteristics and modern network environment, and has strong high security presentation, good consistency and high security is proposed.
引文
1.鲍军鹏,张选平.人工智能导论[M].北京:机械工业出版社,2010
2.鲍新中,刘澄.一种基于粗糙集的权重确定方法[J].管理学报,2009,6(6):729-732
3.曹露文,马瑜.ID3算法在汽车故障诊断中的应用[J].计算机工程应用技术,2012,8(29):7107-7114
4.长孙妮妮,张毅坤,华灯鑫,邹子夏,陈浩.一种基于B+树的混合索引结构[J].计算机工程,2012,38(14):35-37,40
5.陈长征,王楠.遗传算法中交叉和变异概率选择的自适应方法和作用机理[J].控制理论与应用,2002,19(1):41-43
6.陈朝阳,张代胜,任佩红.CBR诊断系统实例获取的合成相似性度量方法[J].机械工程学报,2004,40(5):48-51
7.陈国良.遗传算法及其应用[M].北京:人民邮电出版社,2001
8.陈汉杰,周增强.苹果综合管理技术问答精编[M].河南:河南科学技术出版社,2012
9.陈凌，程中华，曾慧燕.基于案例推理的RCM分析系统中案例检索研究[J].计算机工程与设计,2012,33(2):581-585
10.陈淑慧,梁伟雄.属性约简方法在中医证候数据挖掘中的比较应用[J].中医杂志,2012,53(4):321-323,330
11.陈曦,雷健,傅明.基于改进遗传算法的粗糙集属性约简算法[J].计算机工程与设计,2010,31(3):602-604,608
12.陈艳平.番茄病虫害诊断专家系统知识库的构建[J].中国科技信息,2007,(1):68-69
13.楚燕杰.苹果病虫害诊治原色图谱[M].北京:科学技术文献出版社,2011
14.董海鹏,高延滨,毛奔.基于故障树与案例相结合的故障诊断方法[J].应用科技,2010,37(11):60-64
15.房文娟.基于案例推理技术的果树病害预测系统平台的设计与实现.[硕士学位论文].安徽:安徽农业大学,2005
16.冯斌,邢美凤,邓嵩.基于模糊综合评判的核桃病虫害诊断方法的研究初探[J].山东农业大学学报(自然科学版),2006,37(2):215～219
17.冯冬青,王非,马雁.遗传算法中选择交叉策略的改进[J].计算机工程,2008,34(19):189-191
18.冯洁,廖宁放,赵波,罗永道,李宝聚,戴志福.多光谱成像技术诊断植物病虫害的人工神经网络模型[J].光学技术,2008,34(5):717-720
19.付小军,盛鑫,马进,张毅,徐进.植物病害生物防治概述[J].陕西农业科学，2011,4:138-139,168
20.高济.人工智能高级技术导论[M].北京:高等教育出版社,2009
21.葛继科,邱玉辉,吴春明,蒲国林.遗传算法研究综述[J].计算机应用研究,2008,25(10):2911-2916
22.葛中一.农业专家系统的应用及发展[J].山东师范大学学报(自然科学版),2008,23(4):150-153
23.谷淑娟，高学东，孙冉.一种改进的CBR案例检索相似性度量模型[J].中国管理信息化,2011,14(9):50-55
24.顾东晓,李兴国,梁昌勇,李锋刚.案例检索及权重优化方法研究及应[J].系统工程学报,2009,24(6):764-768
25.郭瑞杰,程学旗,许洪波.一种基于动态平衡树的在线索引快速构建方法[J].计算机研究与进展,2008,45(10):1769-1775
26.贺跃,郑建军,朱蕾.一种基于熵的连续属性离散化算法[J].计算机应用,2005,25(3):637-638,651
27.侯居茌,梁莹,任长志.多变量连续属性离散化方法[J].模拟识别与人工智能,2011,24(6):792-797
28.胡卉颖,钟智,元昌安,陆建波,袁晖.基于基因表达式编程的粗糙集属性约简研究[J].广西师范大学学报,2012,30(2):23-28
29.胡运发.数据索引与数据组织模型及其应用[M].上海:复旦大学出版社,2012
30.花海洋,赵怀慈.一种新的无监督连续属性离散化方法[J].计算机工程与应用,2011,47(6):208-211
31.黄浩,施泽生,蔡洪滨.基于CBR系统事例检索算法的研究[J].计算机应用,2003,23(6):13-16
32.黄继鸿,雷战波,李欣苗.基于遗传禁忌算法的案例检索策略[J].系统工程理论方法应用,2004,13(1):10-13
33.黄宇达,范太华.决策树ID3算法的分析与优化[J].计算机工程与设计,2012,33(8):3089-3093
34.黄玉基,魏伟杰,曾文.基于事例推理系统中检索策略的分析与研究[J].东北大学学报（自然科学版）,2006,27(1):33-36
35.纪希禹,韩秋明,李微.数据挖掘技术应用实例[M].北京:机械工业出版社,2009
36.降惠,李杰.农业专家系统应用现状与前景展望[J].山西农业科学,2012，40(1)：76-78
37.李朝东,崔国贤,盛畅,谢宁,李旺.农业专家系统的发展概况与展望[J].农业网络信息,2009,(3):4-12
38.李春娥,陈全功.多Agent技术在病害诊断系统中的应用[J].计算机工程,2008,34(15):182-184
39.李凡,刘启和,叶茂,杨国纬.不一致决策的知识约简方法研究[J].控制与决策,2006,21(8):857-862
40.李锋刚,倪志伟,杨善林,黄玲.案例推理中属性约简及其性能评价[J].清华大学学报,2006,46(S1):1025-1029
41.李锋刚.基于案例推理的智能决策技术[M].安徽:安徽大学出版社,2011
42.李海芳,魏晓艳,陈俊杰.多维优化案例推理检索算法研究[J].计算机工程与应用,2008,44(25):157-160
43.李浩.XML及其相关技术[M].北京:清华大学出版社,2012
44.李建洋,陈雪云,刘慧婷,谢秀珍.基于案例推理中案例表示的研究[J].合肥学院学报,2007,17(3):26-29
45.李凯，袁鹤.植物病害生物防治概述[J].山西农业科学,2012，40（7）：807-810
46.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
47.李琼玉.基于案例推理的水稻病虫害诊断专家系统的研究与实现.[硕士学位论文].上海:海海洋大学,2010
48.李泉,朱凤荣.我国农业专家系统研究进展[J].河南机电高等专科学校学报,2008,16(6):45-47,50
49.李书全,孙雪,孙德辉,边伟朋.遗传算法中的交叉算子的述评[J].计算机工程与应用,2012,48(1):36-39
50.李伟生,易哲.基于遗传算法的粗糙集属性约简算法[J].微电子学与计算机,2010,27(3):71-74
51.李颖.ebXML技术规范及其在商业信息系统的应用[M].北京:中国标准出版社,2007
52.刘波,潘久辉.基于推进策略的遗传分类方法研究[J].计算机应用研究,2005,22(4):114-115,142
53.刘丹,孔少华,陆伟.XML检索研究综述[J].知识组织与知识管理,2010,4:24-34
54.刘芳,姚莉,王长缨,张维明.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
55.刘建炜,燕路峰.知识表示方法比较[J].计算机系统应用,2010,20(3):242-246
56.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):16-19
57.刘静.基于粗糙集理论的连续值属性离散化方法研究.[硕士学位论文].重庆：重庆邮电大学,2009
58.刘军弟,霍学喜,韩明玉,刘天军.中国苹果产业发展现状及趋势分析[J].北方园艺,2012,(20)：164-168
59.刘莉.基于案例推理的砀山酥梨黑星病预测系统研究.[硕士学位论文].安徽:安徽农业大学,2004
60.刘琴,刘翼,何月秋,林泋君,姜开梅.我国植物病害生物防治综述[J].安徽农学通报,2012,18(7):67-69
61.刘晴蕊,何东健,张宏鸣,朱珊娜,郭云忠.苹果病害智能诊断方法研究与设计[J].农机化研究,2011,(4):76-78,84
62.刘尚辉,曾文.基于案例推理技术在医学病理诊断系统中的应用研究[J].中国医学工程,2012,20(6):139-141
63.刘业政,焦宁,姜元春.连续属性离散化算法比较研究[J].计算机应用研究,2007,24(9):28-30,33
64.刘毅忠,李果.基于案例推理的电力设备多故障诊断方法[J].学报:自然科学版,2012,4(1):75-80
65.柳玉,贲可荣.案例推理的故障诊断技术研究综述[J].计算机科学与探索,2011,5(10):865-879
66.柳玉,贲可荣.故障案例的聚类检索及相关性评估方法研究[J].计算机科学与探索,2012,6(6):545-556
67.路杨,何欣,杜娟娟.基于灰色理论的故障案例检索算法[J].计算机工程,2008,34(9):28-29,32
68.罗忠良,王克运,康仁科,郭东明.基于案例推理系统中案例检索算法的探索[J].计算机工程与应用,2005,(25):230-232
69.苗夺谦.Rough Set理论中连续属性的离散化方法[J].自动化学报,2001,27(3):296-302
70.明博,赖军臣,王娜,谢瑞芝,王克如,李少昆.基于图像规则的玉米病害诊断系统的设计与实现[J].玉米科学,2009,17(6):132～135
71.倪志伟,倪丽萍,叶红云,吴昊.基于案例库维护的案例推理分类技术[J].合肥工业大学学报(自然科学版),2007,30(12):1651-1655
72.倪志伟,蔡庆生.范例推理系统中的范例维护[J].小型微型计算机系统,2003,24(10):1825-1828
73.潘有能.XML挖掘:聚类、分类与信息提取[M].浙江:浙江大学出版社,2012
74.乔丽,姜慧霖,贾世杰.基于改进K-means聚类的案例检索策略[J].计算机工程,2011,37(5):193-195
75.秦玉平,杨兴凯.基于案例推理的区间属性相似度研究[J].辽宁师范大学学报（自然科学版）,2006,29(4):442-445
76.邱德文我国植物病害生物防治的现状及发展策略[J].植物保护,2010,36(4):15-18
77.任凯,浦金云.基于案例属性特征区间相似度的改进算法研究[J].控制与决策,2010,25(2):307-310
78.任艳艳.果树病害防治的原理和方法[J].河北林业科技,2008,(5):85
79.盛畅,崔国贤.专家系统及其在农业上的应用与发展[J].农业网络信息,2008,(3):4-7
80.石军.基于XML的智能信息处理研究.[博士学位论文].陕西:西安电子科技大学,2003
81.史忠植.高级人工智能技术[M].北京:科学出版社,2011
82.孙传恒,唐启义,唐洁,程家安,杨信廷.水稻病虫害诊断模型构建及系统的实现[J].浙江农业学报,2006,18(2):72～76
83.孙岩清,尹树华,林初善.基于粗糙集的CBR系统案例检索策略[J].电讯技术,2010,50(5):23-27
84.汤廷孝,刘勇,黄翔,廖文和.基于覆盖和适应度的CBR实例库维护[J].华南理工大学学报(自然科学版),2005,33(12):61-65
85.汤文宇,李玲娟CBR方法中的案例表示和案例库的构造[J].西安邮电学院学报,2006,11(5):75-78
86.唐建军,王映龙,彭莹琼,李志平.BP神经网络在水稻病虫害诊断中的应用研究[J].安徽农业科学,2010,38(1):199-204
87.唐健元,杨志敏,杨进波,黄钦,吴春芳,冯毅.临床研究中缺失值的类型和处理方法研究[J].中国卫生统计,2011,28(3):338-343
88.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
89.陶志,许宝栋,汪志伟,李冉.基于遗传算法的粗糙集知识简约方法[J].系统工程,2003,21(4):116-122
90.田世恩.苹果综合管理技术问答精编[M].北京:中国农业科学技术出版社,2011
91.王爱茹,刘书华,曹克强,邝朴生.基于规则和图形的苹果、梨病虫害诊断及防治专家系统[J].河北农业大学学报,1999,22(1):60-62
92.王波,宋东,姜华男.基于粗集的CBR故障诊断案例的检索方法研究[J].计算机测量与控制,2007,15(11):1430-1433
93.王国胤,姚一豫,于洪.粗糙集理论与应用研究综述[J].计算机学报,2009,32(7):1229-1246
94.王国胤,于洪,杨大春.基于条件信息熵的决策表约简[J].计算机学报,2002,25(7):1-8
95.王洪强.XML索引及查询处理技术研究.[博士学位论文].黑龙江:哈尔滨工业大学,2009
96.王文辉,周东华.基于遗传算法的一种粗糙集知识约简算法[J].系统仿真学报,2001,13:91-93,96
97.王晓丽,周国民.基于近红外光谱技术的农作物病害诊断[J].农机化研究,2010,(6):171-174
98.王晓亮,刘西拉.基于事例推理系统中的模糊检索[J].上海交通大学学报,2007,41(11):1783-1787
99.王雪松,程玉虎.机器学习理论、方法及应用[M].北京:科学出版社,2009
100.王衍安,李明,王丽辉,刘士勇,闻志强,于庆燕.果树病虫害诊断与防治专家系统知识库的构建[J].山东农业大学学报(自然科学版),2005,36(3):475-480
101.王媛.苹果病虫害智能诊断系统的构建[J].农业图书情报学刊,2006,18(9):10-12,15
102.王悦,范君辉,田书格.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):42-46
103.魏晓艳.多维优化案例推理检索算法研究.[硕士学位论文].山西:太原理工大学,2008
104.温亮宝,刘红霞.基于案例推理的森林病害诊断方法探讨[J].安徽农学通报,2007,13(18):173-175
105.吴华瑞,赵春江,尹宝才.基于启发式搜索的农作物病虫害诊断方法[J].微计算机信息(测控自动化),2010,26(6-1):18-20
106.徐舟.遗传算法在基于事例推理中的应用研究.[硕士学位论文].辽宁:东北大学,2007
107.许向阳,李明胜.位图索引的设计与实现[J].微计算机应用,2006,27(2):188-191
108.薛金国,尤扬,黄广远.园林植物病害诊断与防治[M].北京:中国农业大学出版社,2009
109.杨传健,葛浩,汪志圣.基于粗糙集的属性约简方法研究综述[J].计算机应用研究,2012,29(1):16-20
110.杨涛.基于本体的案例推理系统框架研究.[硕士学位论文].江苏:南京航空航天大学,2006
111.姚旭国,郑可锋,祝利莉,胡为群,叶少挺,张浩,张小斌.一种新型知识表示在诊断型农业专家系统中的实现[J].农业网络信息,2008,(6):9-11,34
112.姚旭国,郑可锋,祝利莉,胡为群,叶少挺,张浩,张小斌.诊断型农业专家系统推理机制的设计与实现[J].农业网络信息,2008,(5):58-60
113.叶进军.基于CBR的数控设备故障诊断系统知识表示[J].北京航空航天大学学报,2011,37(12):1557-1561
114.于锟,刘知贵,黄正良.粗糙集理论应用中的离散化方法综述[J].西南科技大学学报,2005,20(4):32-36
115.余焰军,王英林,张申生.知识管理框架ReKM在农业病虫害诊断中的应用[J].计算机仿真,2006,23,(12):281-285
116.袁涛,孔蕾蕾.统一建模语言UML[M].北京:清华大学出版社,2009
117.张斌成.中国古代的植物病害治理[J].华中师范大学学报(自然科学版),1988,22(3):336-341
118.张光前,邓贵仕,吕文颜.基于粗糙集的归纳推理检索算法[J].计算机工程,2003,29(16):23-24,105
119.张宏,高长松.C4.5算法对ID3算法的改进[J].工程技术,2012.13:116,118
120.张晶,陈褔生.知识管理中的基于XML的知识存储[J].计算机应用研究,2006,(1):69-70,102
121.张丽娟.基于案例推理技术(CBR)在人参病虫害专家系统中的应用.[硕士学位论文].吉林:长春理工大学,2003
122.张腾飞,王锡淮,叶银忠,肖健梅.粗糙集理论在故障诊断中的应用综述[J].上海海事大学学报,2005,26(4):20-25
123.张文静,卢海霞,杨捧,马冬来,徐静.基于关联规则的农业专家系统知识库的建立[J].农机化研究,2008,(7):93-95
124.张莹,陈桂芬.基于本体的玉米领域知识表示构建研究[J].农业网络信息,2012,(3):5-7,41
125.张瑜,娄卉芳,文良浩,熊颉.一种改进的遗传算法交叉策略[J].湖南科技大学学报(自然科学版),2012,27(1):94-97
126.张玉聚,李洪联,张振臣等.农业病虫草害防治新技术精解:中国果树病虫害原色图解[M].北京:中国农业科学技术出版社,2010
127.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
128.赵林峰,熊兴耀,高建亮.专家系统在我国农业上的应用[J].农业网络信息,2007,(4):38-40
129.郑佩.基于案例推理的故障诊断技术研究.[硕士学位论文].湖北:华中科技大学,2008
130.郑涛.粗集理论属性约简的关键技术研究[J].计算机光盘软件与应用,2012,(14):101,103
131.周凡程,吴孟达,王丹.基于Shadowed Sets的连续属性离散化[J].模糊系统与数学,2012,26(2):120-128
132.周凯波,冯珊,李锋.基于案例属性特征的相似度计算模型研究[J].武汉理工大学学报信息与管理工程版,2003,25(1):24-27
133.周习风,赵怀慈,史泽林.基于等级相关的非分类案例检索[J].信息与控制,2010,39(5):542-546
134.周智浩,王宏燕.智能化农业专家系统开发设计与研究[J].现代化农业,2011,(10):48-50
135. Agnar A,Enric P. Case-based Reasoning:Foundational Issues,Methodlogical Variations, andSystem Approachs[J]. AI Communications,1994,7(1):39-59
136. Ashley. Modeling legal argument: Reasoning with cases and hypotheticals[M]. Cambridge,MA: MIT Press, Bradford Books,1990
137. Avesani P,Blanzieri E,Ricci F. Advanced metrics for class-driven similarity search[C]. InProceedings Database and Expert Systems Applicatios1999,Tenth International Workshopon,1999:223-227
138. Bareiss. Examplar-based knowledge acquisition: A unified approach to conceptrepresentation, classification, and learning Boston: Academic Press,1989
139. C Chan,YE loannidis. Bitmap Index Design and Evaluation[C]. In Proceedings ACM SIGMODInternational Conference on Management of Data.June1998, Seatle,Washington,USA
140. Castro J L,Navarro M, Sanchez J M et al. Loss and gain functions for CBR retrieval[J].Information Sciences,2009,179(11):1738-1750
141. Chakraborti S. Beresi C U, Wiratunga N, et al. A Simple approach towards visulizing andevaluating complexity of textual Case Base[C]. ICCBR2007Workshop on Textual Case-basedReasoning:Beyond Retrieval.Heidelberg, Germany:Springer,2007:104-119
142. Chee-Yong Chan, Yannis E Ioannidis. An Efficient Bitmap Encoding Scheme for SelectionQuery. In Proceedings of SIGMOD1999. ACM Press,1999
143. David B.Leake(Edited) Case-Based Reasoning－Experiences,Lessons&Future Directions[M].AAAI Press/The MIT Press,1996
144. Dominik S, Wojciech Z. Attribute reduction in the bayesian version of variable precisionrough set model[J]. Electronic Notes in Theoretical Computers Science,200382(4):1-11
145. Dougherty J,Kohavi R,Sahami M. Supervised and Unsupervised Discretization of ContinousFeature[C]. Proceedings of the12th International Conference on MachineLearning,Edinbrugh, UK,1995:194-202
146. Fagin R, Kumar R, Mahdian M. et al. Comparing partial rankings[J]. SIAM Journal on DiscreteMathematics,2006,20(3):628-648
147. Fayyad U M,Irani K B. Multi-interval Discretization of Continuous-valued Attributes forClassification Learning[J]. In Proceedings of the13th International Joint Conference onArtificial Intelligence,Morgan Kaufnann,1992:1022-1027
148. Filho R J L. Genetic algorithm programming environments[J]. Computer,1994,26(6):29-43
149. Finnie Gavin, Sun Zhao hao R5Model for case-based reasoning[J]. Knowledge-BasedSystems,2003,16:59-65
150. G. Antoshenkov. Byte-aligned bitmap compression[J]. Technical Report, Oracles.SSBDM2007
151. Gladun A,Rogushina J. An application of intelligent techniques and semantic Webtechnologies in e-learning enviroments[J]. Expert Systems withApplications,2009,36(2):1992-1931
152. Goodman. CBR in battle planning[C]. In Proceedings: Workshop on case-based reasoning(DARPA), Pensacola Beach, Florida. San Mateo, CA: Morgan Kaufmann,1989
153. Gupta K M,Montezemi A R. Empirical Evaluation of Retrieval in Case-based ReasoningSystem Using Modified Cosine Matching Function[J]. IEEE Transactions on Systems,Man andCybernetics,1997,27(5):601-612
154. Hammond. Case-Based planning: Viewing planning as memory task[M]. Boston:Academic Press,1989
155. HAN Jiawei, KaInber M. Data Mining-Concepts and Techniques[M]. San Francisco,CA:MorganKaufmann,2001
156. Hennessy D,Hinkle D. Applying Case-based Reasoning to Autoclave Loading[J]. IEEEExpert,1992,7(5):21-26
157. Hinrichs&Kolodner. The roles of adaptation in case-based design[C]. In Proceedings ofAAAI-91. Cambridge, MA: AAAI Press/MIT Press,1991
158. Holte R C. Very Simple Classification Rules Perform Well on Most Commonly UsedDatasets[J]. Machine Learning,1993,(11):63-90
159. Huang Darong, Song Jun. A new method of falut diagnosis based on attribute significance ofmulti-field experts database[J]. Journal of Computer Science,2007,34(4):231-235
160. Hullermeier E. Credible case-based inference usingsimilarity profiles[J]. IEEE Transactions onKnowledge and data Engineering,2007,19(6):847-858
161. Hyunchul Ahn,Kyoung-jae Kim. A case-based reasoning syetem with the two-dimensionalreduction technique for customer classification[J]. Expert Systems WithApplications,2007,32:1011-1019
162. Ian Watson&Farhi Marir. Case-Based Reasoning: A Review. AI-CBR, Dept. of ComputerScience, University of Auckland, New Zealand,1994
163. Janet Kolodner. Case-Based Reasoning[M]. Morgan Kaufmann Publishers，nc，1993
164. Jani H M,Lee S P. Applying case reuse and rule-based reasoning in object-orientedapplication framework documentation:analysis and design[C]. In Proceedings of theconference on Human System Interaction,Krakow,Ploand,2008:597-602
165. Kim Kyoung Jae, Han Ingoo. Maintaining case-based reasoning systems using a geneticalgorithms approach[J]. Expert Systems with Applications,2001,21:139-145
166. Kohavi R. John G. Wrappers for feature selection[J]. ArtificialIntelligence,1997,(1-2):273-324
167. Kolodner J L. An Introduction to Case-based Reasoning[M]. San Franciso,California:MorganKaufmann Publisher,1993
168. Koton. Reasoning about evidence in casual[C].explanation In Proceedings of AAAI-88.Cambridge, MA: AAAI Press/MIT Press,1988
169. Kryszkiewicz M. Comparative Study of Alternative Types of Knowledge Reductions inInconsistent Information Systems[J]. Intelligent Systems,2001,16(1):105-120
170.Lenarcik A, Piasta Z. Discretization of Condition Attributes Space Intelligent Decision Support.Kluwer:Roman Slowinski,1992,373-389
171. Li Yan, Simon C K Shin, Sankar K pal. Combining Feature Reduction and Case Selection inBuilding CBR Classifiers[J]. IEEE Transactions on Knowledge and dataEngineering,2006,18(3):415-429
172. Lingras P J,Yao Y Y. Data mining using exensions of the rough set model[J]. Journal of theAmerican Society for Information Science,1998,49(5):415-422
173. Liu S L,Shi W G. Routh set based intelligence diagnostic system for valves in reciprocatingpumps[C]. IEEE International Conferences on Systems,Man,and Cybernetics,2001:353-358
174. Lv Yuejin,Li Jinhai. A quick algorithm for reduction of attribute in information systems[C]. InProceedings of the1st International Symposium on Data,Privacy andE-Commerce,2007:98-100
175. Maria Aalamo,Elisabet Golobardes. Rough Sets Reduction Techniques for Case-basedReasoning[C]. In Proceeding of Anon International Conference on Case-based Reasoning
2001.Berlin:Springer,2005:467-482
176. Nguyen S H,Skow ron A. Quantization of real values attributes, rough set and Booleanreasoning approaches[C]. Proceedings of the2nd Joint Annual Conference on InformationScience, Wrightsville Beach,NC,1995:34-37
177. P’Oneil. Model204architecture and performance[C]. In Proceedings of the2ndinternational Workshop on High Performace Transaction Systems,September28-30, Asilomar,CA,USA,1987,40-59
178. Paw lak Z. Rough Set[J]. International Journal of Computer and InformationSciences,1982,(11):341-356
179. Paw lak Z. Rough Sets-theoretical aspects of reasoning about data[M]. Dordrecht: KluwerAcademic Publishers,1991:9-30
180. Paw lak Z. Rough set theory and its application to data analysis[J]. Cybernetics andSystems,1998,29(9):661-668
181. Pawlak Z,Skowron A. Rough sets and Boolean reasoning[J]. InformationSciences,2007,17(7):41-73
182. pawlak Z. Rough Sets:Probability Versus Deterministic Aprroach[J]. Man-machineStudies,1988,29(2):81-95
183. Pentland P. Fractal-based description of natrual scenes[J]. IEEE Transaction on Patternanalysis and Machine Intelligence,1984,6(6):661-674
184. Piliouras N, Kalatzis I,Theocharakis P. Development of the probabilistic neuralnetworkp-cubic least squares mapping classifier to assess carotid plaques risk[J]. pattemRecognition Letters,2004,25(2):249-258
185. Polikar R,Udpa L, Upda S S. An International Learning Algorithm for Supervised NeuralNetworks[J]. IEEE Transactions on Systems,2001,31(4):497-508
186. Quinlan J R. Induction on decision trees[J]. Machine Learning,1986,(1):81-106
187. Quinlan J R. C4.5: Programes for machine learing[D].[S.l.]:Morgan Kaufmann,1993
188. Sanchez G f, Garcia V r,Bejar M R. An ontology,intelligent anent-based framewrod for theprovision of semantic Web services[J]. Expert Systems withApplications,2009,36(2):3167-3187
189. Serena H C,Anthony J J,Norton J P. Artificial intelligence techniques:an introduction to theiruse for modelling environmental systems[J]. Mathematics and Computers inSimulation,2008,78(23):379-400
190. Sharma S,Sleeman D. A Case-Base Differential Diagnosis Aide for Knowledge Acquisition andKnowledge Refinement[C]. In Proceedings of the3rd Eruopean Working Session onLearning,1998,(1):201-210
191. Shi Zhongzhi. Advanced artificial intelligence[M]. Beijing:Science Press,2006
192. Shipp C A, Kuncheva L I. Relationships Between Combination Methods and Measures ofDiversity in Combing Classifiers[J]. Information Fusion,2002,3(2):135-148
193. Slonim T Y, Schneider M. Design issues in fuzzy case-based reasoning[J]. Fuzzy Sets andSystems,2001,117(2):251-267
194. Studer R,Benjamins V R,Fensel D. Knowledge Engineering,Principles and Methods[J]. Dataand Knowledge Engineering,1998,25(1-2):161-197
195. Su Zhitong,Li Jinhong. Attibute reduction algorithm based on discernibility matrix and itsapplication[J]. Computer Engineering and Applications,2010,46(6):120-124
196. Tsumoto S. Automated discovery of positive and negativa knowledge in clinical databasesbased on rough set model[J]. IEEE EMB Magazine,2000,19(4):56-62
197. Tuan H,Apkarian P,Nguyen T. Robust and reduced-order filtering:New LMI-basedcharcaterizations and methods[J]. IEEE Transactions on SignalProcessing,2001,49:1688-1689,2975-2984
198. Tumer M B,Demirm C. A genetic approach to data dimensionality reduction using a specialinitial population[C]. In Proceedings of the1st International Work Conference on theInterplay between Natural and Artificial Computation.Berlin:Springer,2005:310-316
199. WangJue, Yuan Xiaohong,Shi Chunyi, et al. Discussions on knowledge representation[J].Chinese Journal of Computers,1995,18(3):212-224
200. Watson I. Case-based Reasoning is a Mothodology not a Technology[J]. Knowledge-BasedSystems,1999,12(5,6):303-308
201. Wu Xiaodong, Vipin K,=.,Quinlan J R.,et. Top10Algorithms in Data Mining[J]. KnowledgeInformation System,2008,14(1):1-37
202. Zhong Shisheng,Wang Tichun,Wangwei. Class retrieval and matching model ofmulti-hierarchy case based on subspace method[J]. China MechanicalEngineering,2009,20(7):767-774
203. Zhou Xifeng,Zhao Huaici,Shi Zelin. Non-classification case retrieval based on rankcorrelation[J]. Information and Control,2010,39(5):542-547
2.鲍新中,刘澄.一种基于粗糙集的权重确定方法[J].管理学报,2009,6(6):729-732
3.曹露文,马瑜.ID3算法在汽车故障诊断中的应用[J].计算机工程应用技术,2012,8(29):7107-7114
4.长孙妮妮,张毅坤,华灯鑫,邹子夏,陈浩.一种基于B+树的混合索引结构[J].计算机工程,2012,38(14):35-37,40
5.陈长征,王楠.遗传算法中交叉和变异概率选择的自适应方法和作用机理[J].控制理论与应用,2002,19(1):41-43
6.陈朝阳,张代胜,任佩红.CBR诊断系统实例获取的合成相似性度量方法[J].机械工程学报,2004,40(5):48-51
7.陈国良.遗传算法及其应用[M].北京:人民邮电出版社,2001
8.陈汉杰,周增强.苹果综合管理技术问答精编[M].河南:河南科学技术出版社,2012
9.陈凌，程中华，曾慧燕.基于案例推理的RCM分析系统中案例检索研究[J].计算机工程与设计,2012,33(2):581-585
10.陈淑慧,梁伟雄.属性约简方法在中医证候数据挖掘中的比较应用[J].中医杂志,2012,53(4):321-323,330
11.陈曦,雷健,傅明.基于改进遗传算法的粗糙集属性约简算法[J].计算机工程与设计,2010,31(3):602-604,608
12.陈艳平.番茄病虫害诊断专家系统知识库的构建[J].中国科技信息,2007,(1):68-69
13.楚燕杰.苹果病虫害诊治原色图谱[M].北京:科学技术文献出版社,2011
14.董海鹏,高延滨,毛奔.基于故障树与案例相结合的故障诊断方法[J].应用科技,2010,37(11):60-64
15.房文娟.基于案例推理技术的果树病害预测系统平台的设计与实现.[硕士学位论文].安徽:安徽农业大学,2005
16.冯斌,邢美凤,邓嵩.基于模糊综合评判的核桃病虫害诊断方法的研究初探[J].山东农业大学学报(自然科学版),2006,37(2):215～219
17.冯冬青,王非,马雁.遗传算法中选择交叉策略的改进[J].计算机工程,2008,34(19):189-191
18.冯洁,廖宁放,赵波,罗永道,李宝聚,戴志福.多光谱成像技术诊断植物病虫害的人工神经网络模型[J].光学技术,2008,34(5):717-720
19.付小军,盛鑫,马进,张毅,徐进.植物病害生物防治概述[J].陕西农业科学，2011,4:138-139,168
20.高济.人工智能高级技术导论[M].北京:高等教育出版社,2009
21.葛继科,邱玉辉,吴春明,蒲国林.遗传算法研究综述[J].计算机应用研究,2008,25(10):2911-2916
22.葛中一.农业专家系统的应用及发展[J].山东师范大学学报(自然科学版),2008,23(4):150-153
23.谷淑娟，高学东，孙冉.一种改进的CBR案例检索相似性度量模型[J].中国管理信息化,2011,14(9):50-55
24.顾东晓,李兴国,梁昌勇,李锋刚.案例检索及权重优化方法研究及应[J].系统工程学报,2009,24(6):764-768
25.郭瑞杰,程学旗,许洪波.一种基于动态平衡树的在线索引快速构建方法[J].计算机研究与进展,2008,45(10):1769-1775
26.贺跃,郑建军,朱蕾.一种基于熵的连续属性离散化算法[J].计算机应用,2005,25(3):637-638,651
27.侯居茌,梁莹,任长志.多变量连续属性离散化方法[J].模拟识别与人工智能,2011,24(6):792-797
28.胡卉颖,钟智,元昌安,陆建波,袁晖.基于基因表达式编程的粗糙集属性约简研究[J].广西师范大学学报,2012,30(2):23-28
29.胡运发.数据索引与数据组织模型及其应用[M].上海:复旦大学出版社,2012
30.花海洋,赵怀慈.一种新的无监督连续属性离散化方法[J].计算机工程与应用,2011,47(6):208-211
31.黄浩,施泽生,蔡洪滨.基于CBR系统事例检索算法的研究[J].计算机应用,2003,23(6):13-16
32.黄继鸿,雷战波,李欣苗.基于遗传禁忌算法的案例检索策略[J].系统工程理论方法应用,2004,13(1):10-13
33.黄宇达,范太华.决策树ID3算法的分析与优化[J].计算机工程与设计,2012,33(8):3089-3093
34.黄玉基,魏伟杰,曾文.基于事例推理系统中检索策略的分析与研究[J].东北大学学报（自然科学版）,2006,27(1):33-36
35.纪希禹,韩秋明,李微.数据挖掘技术应用实例[M].北京:机械工业出版社,2009
36.降惠,李杰.农业专家系统应用现状与前景展望[J].山西农业科学,2012，40(1)：76-78
37.李朝东,崔国贤,盛畅,谢宁,李旺.农业专家系统的发展概况与展望[J].农业网络信息,2009,(3):4-12
38.李春娥,陈全功.多Agent技术在病害诊断系统中的应用[J].计算机工程,2008,34(15):182-184
39.李凡,刘启和,叶茂,杨国纬.不一致决策的知识约简方法研究[J].控制与决策,2006,21(8):857-862
40.李锋刚,倪志伟,杨善林,黄玲.案例推理中属性约简及其性能评价[J].清华大学学报,2006,46(S1):1025-1029
41.李锋刚.基于案例推理的智能决策技术[M].安徽:安徽大学出版社,2011
42.李海芳,魏晓艳,陈俊杰.多维优化案例推理检索算法研究[J].计算机工程与应用,2008,44(25):157-160
43.李浩.XML及其相关技术[M].北京:清华大学出版社,2012
44.李建洋,陈雪云,刘慧婷,谢秀珍.基于案例推理中案例表示的研究[J].合肥学院学报,2007,17(3):26-29
45.李凯，袁鹤.植物病害生物防治概述[J].山西农业科学,2012，40（7）：807-810
46.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
47.李琼玉.基于案例推理的水稻病虫害诊断专家系统的研究与实现.[硕士学位论文].上海:海海洋大学,2010
48.李泉,朱凤荣.我国农业专家系统研究进展[J].河南机电高等专科学校学报,2008,16(6):45-47,50
49.李书全,孙雪,孙德辉,边伟朋.遗传算法中的交叉算子的述评[J].计算机工程与应用,2012,48(1):36-39
50.李伟生,易哲.基于遗传算法的粗糙集属性约简算法[J].微电子学与计算机,2010,27(3):71-74
51.李颖.ebXML技术规范及其在商业信息系统的应用[M].北京:中国标准出版社,2007
52.刘波,潘久辉.基于推进策略的遗传分类方法研究[J].计算机应用研究,2005,22(4):114-115,142
53.刘丹,孔少华,陆伟.XML检索研究综述[J].知识组织与知识管理,2010,4:24-34
54.刘芳,姚莉,王长缨,张维明.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
55.刘建炜,燕路峰.知识表示方法比较[J].计算机系统应用,2010,20(3):242-246
56.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):16-19
57.刘静.基于粗糙集理论的连续值属性离散化方法研究.[硕士学位论文].重庆：重庆邮电大学,2009
58.刘军弟,霍学喜,韩明玉,刘天军.中国苹果产业发展现状及趋势分析[J].北方园艺,2012,(20)：164-168
59.刘莉.基于案例推理的砀山酥梨黑星病预测系统研究.[硕士学位论文].安徽:安徽农业大学,2004
60.刘琴,刘翼,何月秋,林泋君,姜开梅.我国植物病害生物防治综述[J].安徽农学通报,2012,18(7):67-69
61.刘晴蕊,何东健,张宏鸣,朱珊娜,郭云忠.苹果病害智能诊断方法研究与设计[J].农机化研究,2011,(4):76-78,84
62.刘尚辉,曾文.基于案例推理技术在医学病理诊断系统中的应用研究[J].中国医学工程,2012,20(6):139-141
63.刘业政,焦宁,姜元春.连续属性离散化算法比较研究[J].计算机应用研究,2007,24(9):28-30,33
64.刘毅忠,李果.基于案例推理的电力设备多故障诊断方法[J].学报:自然科学版,2012,4(1):75-80
65.柳玉,贲可荣.案例推理的故障诊断技术研究综述[J].计算机科学与探索,2011,5(10):865-879
66.柳玉,贲可荣.故障案例的聚类检索及相关性评估方法研究[J].计算机科学与探索,2012,6(6):545-556
67.路杨,何欣,杜娟娟.基于灰色理论的故障案例检索算法[J].计算机工程,2008,34(9):28-29,32
68.罗忠良,王克运,康仁科,郭东明.基于案例推理系统中案例检索算法的探索[J].计算机工程与应用,2005,(25):230-232
69.苗夺谦.Rough Set理论中连续属性的离散化方法[J].自动化学报,2001,27(3):296-302
70.明博,赖军臣,王娜,谢瑞芝,王克如,李少昆.基于图像规则的玉米病害诊断系统的设计与实现[J].玉米科学,2009,17(6):132～135
71.倪志伟,倪丽萍,叶红云,吴昊.基于案例库维护的案例推理分类技术[J].合肥工业大学学报(自然科学版),2007,30(12):1651-1655
72.倪志伟,蔡庆生.范例推理系统中的范例维护[J].小型微型计算机系统,2003,24(10):1825-1828
73.潘有能.XML挖掘:聚类、分类与信息提取[M].浙江:浙江大学出版社,2012
74.乔丽,姜慧霖,贾世杰.基于改进K-means聚类的案例检索策略[J].计算机工程,2011,37(5):193-195
75.秦玉平,杨兴凯.基于案例推理的区间属性相似度研究[J].辽宁师范大学学报（自然科学版）,2006,29(4):442-445
76.邱德文我国植物病害生物防治的现状及发展策略[J].植物保护,2010,36(4):15-18
77.任凯,浦金云.基于案例属性特征区间相似度的改进算法研究[J].控制与决策,2010,25(2):307-310
78.任艳艳.果树病害防治的原理和方法[J].河北林业科技,2008,(5):85
79.盛畅,崔国贤.专家系统及其在农业上的应用与发展[J].农业网络信息,2008,(3):4-7
80.石军.基于XML的智能信息处理研究.[博士学位论文].陕西:西安电子科技大学,2003
81.史忠植.高级人工智能技术[M].北京:科学出版社,2011
82.孙传恒,唐启义,唐洁,程家安,杨信廷.水稻病虫害诊断模型构建及系统的实现[J].浙江农业学报,2006,18(2):72～76
83.孙岩清,尹树华,林初善.基于粗糙集的CBR系统案例检索策略[J].电讯技术,2010,50(5):23-27
84.汤廷孝,刘勇,黄翔,廖文和.基于覆盖和适应度的CBR实例库维护[J].华南理工大学学报(自然科学版),2005,33(12):61-65
85.汤文宇,李玲娟CBR方法中的案例表示和案例库的构造[J].西安邮电学院学报,2006,11(5):75-78
86.唐建军,王映龙,彭莹琼,李志平.BP神经网络在水稻病虫害诊断中的应用研究[J].安徽农业科学,2010,38(1):199-204
87.唐健元,杨志敏,杨进波,黄钦,吴春芳,冯毅.临床研究中缺失值的类型和处理方法研究[J].中国卫生统计,2011,28(3):338-343
88.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
89.陶志,许宝栋,汪志伟,李冉.基于遗传算法的粗糙集知识简约方法[J].系统工程,2003,21(4):116-122
90.田世恩.苹果综合管理技术问答精编[M].北京:中国农业科学技术出版社,2011
91.王爱茹,刘书华,曹克强,邝朴生.基于规则和图形的苹果、梨病虫害诊断及防治专家系统[J].河北农业大学学报,1999,22(1):60-62
92.王波,宋东,姜华男.基于粗集的CBR故障诊断案例的检索方法研究[J].计算机测量与控制,2007,15(11):1430-1433
93.王国胤,姚一豫,于洪.粗糙集理论与应用研究综述[J].计算机学报,2009,32(7):1229-1246
94.王国胤,于洪,杨大春.基于条件信息熵的决策表约简[J].计算机学报,2002,25(7):1-8
95.王洪强.XML索引及查询处理技术研究.[博士学位论文].黑龙江:哈尔滨工业大学,2009
96.王文辉,周东华.基于遗传算法的一种粗糙集知识约简算法[J].系统仿真学报,2001,13:91-93,96
97.王晓丽,周国民.基于近红外光谱技术的农作物病害诊断[J].农机化研究,2010,(6):171-174
98.王晓亮,刘西拉.基于事例推理系统中的模糊检索[J].上海交通大学学报,2007,41(11):1783-1787
99.王雪松,程玉虎.机器学习理论、方法及应用[M].北京:科学出版社,2009
100.王衍安,李明,王丽辉,刘士勇,闻志强,于庆燕.果树病虫害诊断与防治专家系统知识库的构建[J].山东农业大学学报(自然科学版),2005,36(3):475-480
101.王媛.苹果病虫害智能诊断系统的构建[J].农业图书情报学刊,2006,18(9):10-12,15
102.王悦,范君辉,田书格.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):42-46
103.魏晓艳.多维优化案例推理检索算法研究.[硕士学位论文].山西:太原理工大学,2008
104.温亮宝,刘红霞.基于案例推理的森林病害诊断方法探讨[J].安徽农学通报,2007,13(18):173-175
105.吴华瑞,赵春江,尹宝才.基于启发式搜索的农作物病虫害诊断方法[J].微计算机信息(测控自动化),2010,26(6-1):18-20
106.徐舟.遗传算法在基于事例推理中的应用研究.[硕士学位论文].辽宁:东北大学,2007
107.许向阳,李明胜.位图索引的设计与实现[J].微计算机应用,2006,27(2):188-191
108.薛金国,尤扬,黄广远.园林植物病害诊断与防治[M].北京:中国农业大学出版社,2009
109.杨传健,葛浩,汪志圣.基于粗糙集的属性约简方法研究综述[J].计算机应用研究,2012,29(1):16-20
110.杨涛.基于本体的案例推理系统框架研究.[硕士学位论文].江苏:南京航空航天大学,2006
111.姚旭国,郑可锋,祝利莉,胡为群,叶少挺,张浩,张小斌.一种新型知识表示在诊断型农业专家系统中的实现[J].农业网络信息,2008,(6):9-11,34
112.姚旭国,郑可锋,祝利莉,胡为群,叶少挺,张浩,张小斌.诊断型农业专家系统推理机制的设计与实现[J].农业网络信息,2008,(5):58-60
113.叶进军.基于CBR的数控设备故障诊断系统知识表示[J].北京航空航天大学学报,2011,37(12):1557-1561
114.于锟,刘知贵,黄正良.粗糙集理论应用中的离散化方法综述[J].西南科技大学学报,2005,20(4):32-36
115.余焰军,王英林,张申生.知识管理框架ReKM在农业病虫害诊断中的应用[J].计算机仿真,2006,23,(12):281-285
116.袁涛,孔蕾蕾.统一建模语言UML[M].北京:清华大学出版社,2009
117.张斌成.中国古代的植物病害治理[J].华中师范大学学报(自然科学版),1988,22(3):336-341
118.张光前,邓贵仕,吕文颜.基于粗糙集的归纳推理检索算法[J].计算机工程,2003,29(16):23-24,105
119.张宏,高长松.C4.5算法对ID3算法的改进[J].工程技术,2012.13:116,118
120.张晶,陈褔生.知识管理中的基于XML的知识存储[J].计算机应用研究,2006,(1):69-70,102
121.张丽娟.基于案例推理技术(CBR)在人参病虫害专家系统中的应用.[硕士学位论文].吉林:长春理工大学,2003
122.张腾飞,王锡淮,叶银忠,肖健梅.粗糙集理论在故障诊断中的应用综述[J].上海海事大学学报,2005,26(4):20-25
123.张文静,卢海霞,杨捧,马冬来,徐静.基于关联规则的农业专家系统知识库的建立[J].农机化研究,2008,(7):93-95
124.张莹,陈桂芬.基于本体的玉米领域知识表示构建研究[J].农业网络信息,2012,(3):5-7,41
125.张瑜,娄卉芳,文良浩,熊颉.一种改进的遗传算法交叉策略[J].湖南科技大学学报(自然科学版),2012,27(1):94-97
126.张玉聚,李洪联,张振臣等.农业病虫草害防治新技术精解:中国果树病虫害原色图解[M].北京:中国农业科学技术出版社,2010
127.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
128.赵林峰,熊兴耀,高建亮.专家系统在我国农业上的应用[J].农业网络信息,2007,(4):38-40
129.郑佩.基于案例推理的故障诊断技术研究.[硕士学位论文].湖北:华中科技大学,2008
130.郑涛.粗集理论属性约简的关键技术研究[J].计算机光盘软件与应用,2012,(14):101,103
131.周凡程,吴孟达,王丹.基于Shadowed Sets的连续属性离散化[J].模糊系统与数学,2012,26(2):120-128
132.周凯波,冯珊,李锋.基于案例属性特征的相似度计算模型研究[J].武汉理工大学学报信息与管理工程版,2003,25(1):24-27
133.周习风,赵怀慈,史泽林.基于等级相关的非分类案例检索[J].信息与控制,2010,39(5):542-546
134.周智浩,王宏燕.智能化农业专家系统开发设计与研究[J].现代化农业,2011,(10):48-50
135. Agnar A,Enric P. Case-based Reasoning:Foundational Issues,Methodlogical Variations, andSystem Approachs[J]. AI Communications,1994,7(1):39-59
136. Ashley. Modeling legal argument: Reasoning with cases and hypotheticals[M]. Cambridge,MA: MIT Press, Bradford Books,1990
137. Avesani P,Blanzieri E,Ricci F. Advanced metrics for class-driven similarity search[C]. InProceedings Database and Expert Systems Applicatios1999,Tenth International Workshopon,1999:223-227
138. Bareiss. Examplar-based knowledge acquisition: A unified approach to conceptrepresentation, classification, and learning Boston: Academic Press,1989
139. C Chan,YE loannidis. Bitmap Index Design and Evaluation[C]. In Proceedings ACM SIGMODInternational Conference on Management of Data.June1998, Seatle,Washington,USA
140. Castro J L,Navarro M, Sanchez J M et al. Loss and gain functions for CBR retrieval[J].Information Sciences,2009,179(11):1738-1750
141. Chakraborti S. Beresi C U, Wiratunga N, et al. A Simple approach towards visulizing andevaluating complexity of textual Case Base[C]. ICCBR2007Workshop on Textual Case-basedReasoning:Beyond Retrieval.Heidelberg, Germany:Springer,2007:104-119
142. Chee-Yong Chan, Yannis E Ioannidis. An Efficient Bitmap Encoding Scheme for SelectionQuery. In Proceedings of SIGMOD1999. ACM Press,1999
143. David B.Leake(Edited) Case-Based Reasoning－Experiences,Lessons&Future Directions[M].AAAI Press/The MIT Press,1996
144. Dominik S, Wojciech Z. Attribute reduction in the bayesian version of variable precisionrough set model[J]. Electronic Notes in Theoretical Computers Science,200382(4):1-11
145. Dougherty J,Kohavi R,Sahami M. Supervised and Unsupervised Discretization of ContinousFeature[C]. Proceedings of the12th International Conference on MachineLearning,Edinbrugh, UK,1995:194-202
146. Fagin R, Kumar R, Mahdian M. et al. Comparing partial rankings[J]. SIAM Journal on DiscreteMathematics,2006,20(3):628-648
147. Fayyad U M,Irani K B. Multi-interval Discretization of Continuous-valued Attributes forClassification Learning[J]. In Proceedings of the13th International Joint Conference onArtificial Intelligence,Morgan Kaufnann,1992:1022-1027
148. Filho R J L. Genetic algorithm programming environments[J]. Computer,1994,26(6):29-43
149. Finnie Gavin, Sun Zhao hao R5Model for case-based reasoning[J]. Knowledge-BasedSystems,2003,16:59-65
150. G. Antoshenkov. Byte-aligned bitmap compression[J]. Technical Report, Oracles.SSBDM2007
151. Gladun A,Rogushina J. An application of intelligent techniques and semantic Webtechnologies in e-learning enviroments[J]. Expert Systems withApplications,2009,36(2):1992-1931
152. Goodman. CBR in battle planning[C]. In Proceedings: Workshop on case-based reasoning(DARPA), Pensacola Beach, Florida. San Mateo, CA: Morgan Kaufmann,1989
153. Gupta K M,Montezemi A R. Empirical Evaluation of Retrieval in Case-based ReasoningSystem Using Modified Cosine Matching Function[J]. IEEE Transactions on Systems,Man andCybernetics,1997,27(5):601-612
154. Hammond. Case-Based planning: Viewing planning as memory task[M]. Boston:Academic Press,1989
155. HAN Jiawei, KaInber M. Data Mining-Concepts and Techniques[M]. San Francisco,CA:MorganKaufmann,2001
156. Hennessy D,Hinkle D. Applying Case-based Reasoning to Autoclave Loading[J]. IEEEExpert,1992,7(5):21-26
157. Hinrichs&Kolodner. The roles of adaptation in case-based design[C]. In Proceedings ofAAAI-91. Cambridge, MA: AAAI Press/MIT Press,1991
158. Holte R C. Very Simple Classification Rules Perform Well on Most Commonly UsedDatasets[J]. Machine Learning,1993,(11):63-90
159. Huang Darong, Song Jun. A new method of falut diagnosis based on attribute significance ofmulti-field experts database[J]. Journal of Computer Science,2007,34(4):231-235
160. Hullermeier E. Credible case-based inference usingsimilarity profiles[J]. IEEE Transactions onKnowledge and data Engineering,2007,19(6):847-858
161. Hyunchul Ahn,Kyoung-jae Kim. A case-based reasoning syetem with the two-dimensionalreduction technique for customer classification[J]. Expert Systems WithApplications,2007,32:1011-1019
162. Ian Watson&Farhi Marir. Case-Based Reasoning: A Review. AI-CBR, Dept. of ComputerScience, University of Auckland, New Zealand,1994
163. Janet Kolodner. Case-Based Reasoning[M]. Morgan Kaufmann Publishers，nc，1993
164. Jani H M,Lee S P. Applying case reuse and rule-based reasoning in object-orientedapplication framework documentation:analysis and design[C]. In Proceedings of theconference on Human System Interaction,Krakow,Ploand,2008:597-602
165. Kim Kyoung Jae, Han Ingoo. Maintaining case-based reasoning systems using a geneticalgorithms approach[J]. Expert Systems with Applications,2001,21:139-145
166. Kohavi R. John G. Wrappers for feature selection[J]. ArtificialIntelligence,1997,(1-2):273-324
167. Kolodner J L. An Introduction to Case-based Reasoning[M]. San Franciso,California:MorganKaufmann Publisher,1993
168. Koton. Reasoning about evidence in casual[C].explanation In Proceedings of AAAI-88.Cambridge, MA: AAAI Press/MIT Press,1988
169. Kryszkiewicz M. Comparative Study of Alternative Types of Knowledge Reductions inInconsistent Information Systems[J]. Intelligent Systems,2001,16(1):105-120
170.Lenarcik A, Piasta Z. Discretization of Condition Attributes Space Intelligent Decision Support.Kluwer:Roman Slowinski,1992,373-389
171. Li Yan, Simon C K Shin, Sankar K pal. Combining Feature Reduction and Case Selection inBuilding CBR Classifiers[J]. IEEE Transactions on Knowledge and dataEngineering,2006,18(3):415-429
172. Lingras P J,Yao Y Y. Data mining using exensions of the rough set model[J]. Journal of theAmerican Society for Information Science,1998,49(5):415-422
173. Liu S L,Shi W G. Routh set based intelligence diagnostic system for valves in reciprocatingpumps[C]. IEEE International Conferences on Systems,Man,and Cybernetics,2001:353-358
174. Lv Yuejin,Li Jinhai. A quick algorithm for reduction of attribute in information systems[C]. InProceedings of the1st International Symposium on Data,Privacy andE-Commerce,2007:98-100
175. Maria Aalamo,Elisabet Golobardes. Rough Sets Reduction Techniques for Case-basedReasoning[C]. In Proceeding of Anon International Conference on Case-based Reasoning
2001.Berlin:Springer,2005:467-482
176. Nguyen S H,Skow ron A. Quantization of real values attributes, rough set and Booleanreasoning approaches[C]. Proceedings of the2nd Joint Annual Conference on InformationScience, Wrightsville Beach,NC,1995:34-37
177. P’Oneil. Model204architecture and performance[C]. In Proceedings of the2ndinternational Workshop on High Performace Transaction Systems,September28-30, Asilomar,CA,USA,1987,40-59
178. Paw lak Z. Rough Set[J]. International Journal of Computer and InformationSciences,1982,(11):341-356
179. Paw lak Z. Rough Sets-theoretical aspects of reasoning about data[M]. Dordrecht: KluwerAcademic Publishers,1991:9-30
180. Paw lak Z. Rough set theory and its application to data analysis[J]. Cybernetics andSystems,1998,29(9):661-668
181. Pawlak Z,Skowron A. Rough sets and Boolean reasoning[J]. InformationSciences,2007,17(7):41-73
182. pawlak Z. Rough Sets:Probability Versus Deterministic Aprroach[J]. Man-machineStudies,1988,29(2):81-95
183. Pentland P. Fractal-based description of natrual scenes[J]. IEEE Transaction on Patternanalysis and Machine Intelligence,1984,6(6):661-674
184. Piliouras N, Kalatzis I,Theocharakis P. Development of the probabilistic neuralnetworkp-cubic least squares mapping classifier to assess carotid plaques risk[J]. pattemRecognition Letters,2004,25(2):249-258
185. Polikar R,Udpa L, Upda S S. An International Learning Algorithm for Supervised NeuralNetworks[J]. IEEE Transactions on Systems,2001,31(4):497-508
186. Quinlan J R. Induction on decision trees[J]. Machine Learning,1986,(1):81-106
187. Quinlan J R. C4.5: Programes for machine learing[D].[S.l.]:Morgan Kaufmann,1993
188. Sanchez G f, Garcia V r,Bejar M R. An ontology,intelligent anent-based framewrod for theprovision of semantic Web services[J]. Expert Systems withApplications,2009,36(2):3167-3187
189. Serena H C,Anthony J J,Norton J P. Artificial intelligence techniques:an introduction to theiruse for modelling environmental systems[J]. Mathematics and Computers inSimulation,2008,78(23):379-400
190. Sharma S,Sleeman D. A Case-Base Differential Diagnosis Aide for Knowledge Acquisition andKnowledge Refinement[C]. In Proceedings of the3rd Eruopean Working Session onLearning,1998,(1):201-210
191. Shi Zhongzhi. Advanced artificial intelligence[M]. Beijing:Science Press,2006
192. Shipp C A, Kuncheva L I. Relationships Between Combination Methods and Measures ofDiversity in Combing Classifiers[J]. Information Fusion,2002,3(2):135-148
193. Slonim T Y, Schneider M. Design issues in fuzzy case-based reasoning[J]. Fuzzy Sets andSystems,2001,117(2):251-267
194. Studer R,Benjamins V R,Fensel D. Knowledge Engineering,Principles and Methods[J]. Dataand Knowledge Engineering,1998,25(1-2):161-197
195. Su Zhitong,Li Jinhong. Attibute reduction algorithm based on discernibility matrix and itsapplication[J]. Computer Engineering and Applications,2010,46(6):120-124
196. Tsumoto S. Automated discovery of positive and negativa knowledge in clinical databasesbased on rough set model[J]. IEEE EMB Magazine,2000,19(4):56-62
197. Tuan H,Apkarian P,Nguyen T. Robust and reduced-order filtering:New LMI-basedcharcaterizations and methods[J]. IEEE Transactions on SignalProcessing,2001,49:1688-1689,2975-2984
198. Tumer M B,Demirm C. A genetic approach to data dimensionality reduction using a specialinitial population[C]. In Proceedings of the1st International Work Conference on theInterplay between Natural and Artificial Computation.Berlin:Springer,2005:310-316
199. WangJue, Yuan Xiaohong,Shi Chunyi, et al. Discussions on knowledge representation[J].Chinese Journal of Computers,1995,18(3):212-224
200. Watson I. Case-based Reasoning is a Mothodology not a Technology[J]. Knowledge-BasedSystems,1999,12(5,6):303-308
201. Wu Xiaodong, Vipin K,=.,Quinlan J R.,et. Top10Algorithms in Data Mining[J]. KnowledgeInformation System,2008,14(1):1-37
202. Zhong Shisheng,Wang Tichun,Wangwei. Class retrieval and matching model ofmulti-hierarchy case based on subspace method[J]. China MechanicalEngineering,2009,20(7):767-774
203. Zhou Xifeng,Zhao Huaici,Shi Zelin. Non-classification case retrieval based on rankcorrelation[J]. Information and Control,2010,39(5):542-547
© 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 |