不完备不协调信息条件下的设备智能故障诊断
|
摘要
由于设备结构的复杂导致了故障征兆与故障原因的多样性和故障信息的不完备与不协调,这是目前设备智能故障诊断研究中所面临的一大难题。研究具有不完备、不协调信息的设备故障诊断方法具有重要的现实意义。本文以粗糙集理论为基础,对故障信息系统特别是不完备、不协调决策信息系统的属性约简、规则获取及规则发现进行系统理论研究。通过建立智能推理诊断模型,为复杂设备的故障诊断提供了新的方法和手段。
本学位论文主要作出了以下几个方面的研究工作:
设备故障的不确定主要表现为故障信息的不完备与不协调,其与故障发生的概率有关,研究用事件的自信息来度量事件的不确定性、用信息熵来度量诊断系统不确定性的方法。以完备信息系统为研究对象,提出了基于最大分辨度的启发式属性约简算法,实现了多属性决策信息系统的最优属性约简,为决策信息系统属性约简提供新方法。
针对不完备信息系统,通过研究最高可信度的数据补齐算法,保证了从中获取的规则对诊断决策有尽可能高的支持度。在不完备信息系统规则获取方面,作了两方面的研究工作:一是为从动态增加诊断样本中快速获取诊断规则,提出了一种增量式获取规则算法,该算法能有效地减少获取规则的计算量,节约大量的资源;二是受认识规律的启发,将不完备信息系统分为完备层和非完备层,然后进行层次递阶约简。即先对完备层进行约简,然后将不完备属性根据重要性大小逐层分步约简并提取规则,减小因系统完备化使信息失真而带来的不确定性,理论上证明了分层过程不会增加信息系统的不确定性。
对不协调决策信息系统,将条件属性等价类与决策属性等价类用包含程度来描述,根据包含的程度不同,研究了分布约简、最大分布约简及分配约简算法;分布约简集与原属性集产生的规则有相同的可信度,通过对不协调决策信息系统的分布约简,解决了隐规则发现难的问题,并给出其可信度,这能缓解诊断系统数据量大而知识贫乏的矛盾;同时用最大分布约简算法实现了不完备不协调系统的最优选择。
基于设备结构分解策略将故障特征参数空间和故障空间分割为多个子空间,能有效的降低推理机输入数据的维数。采用信息融合方法研究了智能故障推理机模型,提出了多子神经网络与模糊推理并串融合智能推理机结构模型。用改进的BP算法对并行子网络进行训练,模糊诊断权矩阵能根据样本数据自动生成,使推理机有良好的学习与容错性能,能达到较理想的推理效果。同时讨论了并行子网的组建和BP神经网络结构的优化原则和方法。
在分析装载机远程故障诊断的系统需求的基础上,提出并实现了装载机远程智能故障诊断系统的总体结构、功能模型和计算模型,并用于实际的工程对象。实践表明,该系统能适应不同用户的要求,结构合理、工作稳定、实用性强。
The complexity of equipment structure brings on the diversity between symptom and cause of equipment fault, and fault information turn incomplete and inconsistent which is a huge puzzle in intelligent fault diagnosis of equipment at present. It is important to research fault diagnosis approaches of the equipment with incomplete and inconsistent information. Based on the rough set theory, this dissertation does deep theoretical researchers on attribute reduction, rules acquisition and rules discovery of incomplete and inconsistent decision-making information system. A new method and means is provided for fault diagnosis of the complex equipment by establishing the intelligent diagnosis model.
The major innovations of this dissertation are as follows:
The uncertainty of equipment fault, which important representation is incomplete and inconsistent, relates to probability of fault. The uncertainty measurement method by self-information of the event and the information entropy of the system are put forward respectively. The heuristic attribute reduction algorithm based on maximum discernibility degree is put forward, and then the optimal attribute reduction of the multiattribute decision-making information system is realized.
Aiming at incomplete information system, the completer algorithm based on maximal confidence is studied, the biggest supporting degree of rules which are obtained from incomplete system is ensured. Two algorithms based on rules acquisition in the incomplete decision-making information system are proposed. One is the incremental rules acquisition approach avoiding repeated calculations from the beginning; the other is the hierarchical reduction approach. The first algorithms can not only decrease effectively the computing times of rules acquisition, but also save a mass of computing resources. The second algorithm proposed, enlightened on the recognition laws of human being, divide the whole system into complete attribute level and incomplete attribute level. Firstly, complete attribute level can be reduced using the heuristic attribute reduction algorithm. Secondly, the incomplete attribute can be reduced according to its significance one by one. Lastly, the rules can be extracted rightly. The hierarchical reduction approach can not only decrease the uncertainty because of completeness process, but also not lead the information losing of decision system because of stratifying process.
Through further researches on inconsistent decision-making information system, the depending relationship between the conditional attributes equivalence class and the decision attributes equivalence class is described using inclusion degree. According to the diversity of inclusion degree, the algorithms of distribution reduction, maximum distribution reduction and assignment reduction are researched respectively. The rule reliability by distribute reducing is tantamount with original attribute set. The difficulty to discover hidden rule is overcome and the reliability of hidden rule is given via distributing reduces to inconsistent information system. The hidden rules discovery can relax the conflict between great data size and necessitous knowledge. At the same time, the optimum choice of incomplete fault diagnosis system is offered using the maximum distribution reduction method.
Based on the decomposing strategy of equipment’s structure, the characteristic parameter space and the fault space split multi-subspace can reduced the dimension of input data of reasoning model. The intelligent diagnosis reasoning model is studied using information fuse and the ANN&FR parallel-series hybrid reasoning model by way of changing single-ANN for multi-ANN parallel integration is put up forward also. The learning speed can be improved effectively and the error concussion can be reduced and even be eliminated by adopting improved BP learning algorithm to learn each subnet. The diagnosis weight matrix can be self-adjusted in the process of fuzzy reasoning so as to increase the accuracy of fault diagnosis. At last, the parallel subnet composition principle and the structure optimization means of BP NN are discussed.
Based on analyzing the system requirements of remote intelligent fault diagnosis of loader, the general structure, functional and calculating model of fault diagnosis system of loader is proposed and realized. The practical results show this system can adapt the request of different consumers, structure reasonably, work steadily and practicability well.
本学位论文主要作出了以下几个方面的研究工作:
设备故障的不确定主要表现为故障信息的不完备与不协调,其与故障发生的概率有关,研究用事件的自信息来度量事件的不确定性、用信息熵来度量诊断系统不确定性的方法。以完备信息系统为研究对象,提出了基于最大分辨度的启发式属性约简算法,实现了多属性决策信息系统的最优属性约简,为决策信息系统属性约简提供新方法。
针对不完备信息系统,通过研究最高可信度的数据补齐算法,保证了从中获取的规则对诊断决策有尽可能高的支持度。在不完备信息系统规则获取方面,作了两方面的研究工作:一是为从动态增加诊断样本中快速获取诊断规则,提出了一种增量式获取规则算法,该算法能有效地减少获取规则的计算量,节约大量的资源;二是受认识规律的启发,将不完备信息系统分为完备层和非完备层,然后进行层次递阶约简。即先对完备层进行约简,然后将不完备属性根据重要性大小逐层分步约简并提取规则,减小因系统完备化使信息失真而带来的不确定性,理论上证明了分层过程不会增加信息系统的不确定性。
对不协调决策信息系统,将条件属性等价类与决策属性等价类用包含程度来描述,根据包含的程度不同,研究了分布约简、最大分布约简及分配约简算法;分布约简集与原属性集产生的规则有相同的可信度,通过对不协调决策信息系统的分布约简,解决了隐规则发现难的问题,并给出其可信度,这能缓解诊断系统数据量大而知识贫乏的矛盾;同时用最大分布约简算法实现了不完备不协调系统的最优选择。
基于设备结构分解策略将故障特征参数空间和故障空间分割为多个子空间,能有效的降低推理机输入数据的维数。采用信息融合方法研究了智能故障推理机模型,提出了多子神经网络与模糊推理并串融合智能推理机结构模型。用改进的BP算法对并行子网络进行训练,模糊诊断权矩阵能根据样本数据自动生成,使推理机有良好的学习与容错性能,能达到较理想的推理效果。同时讨论了并行子网的组建和BP神经网络结构的优化原则和方法。
在分析装载机远程故障诊断的系统需求的基础上,提出并实现了装载机远程智能故障诊断系统的总体结构、功能模型和计算模型,并用于实际的工程对象。实践表明,该系统能适应不同用户的要求,结构合理、工作稳定、实用性强。
The complexity of equipment structure brings on the diversity between symptom and cause of equipment fault, and fault information turn incomplete and inconsistent which is a huge puzzle in intelligent fault diagnosis of equipment at present. It is important to research fault diagnosis approaches of the equipment with incomplete and inconsistent information. Based on the rough set theory, this dissertation does deep theoretical researchers on attribute reduction, rules acquisition and rules discovery of incomplete and inconsistent decision-making information system. A new method and means is provided for fault diagnosis of the complex equipment by establishing the intelligent diagnosis model.
The major innovations of this dissertation are as follows:
The uncertainty of equipment fault, which important representation is incomplete and inconsistent, relates to probability of fault. The uncertainty measurement method by self-information of the event and the information entropy of the system are put forward respectively. The heuristic attribute reduction algorithm based on maximum discernibility degree is put forward, and then the optimal attribute reduction of the multiattribute decision-making information system is realized.
Aiming at incomplete information system, the completer algorithm based on maximal confidence is studied, the biggest supporting degree of rules which are obtained from incomplete system is ensured. Two algorithms based on rules acquisition in the incomplete decision-making information system are proposed. One is the incremental rules acquisition approach avoiding repeated calculations from the beginning; the other is the hierarchical reduction approach. The first algorithms can not only decrease effectively the computing times of rules acquisition, but also save a mass of computing resources. The second algorithm proposed, enlightened on the recognition laws of human being, divide the whole system into complete attribute level and incomplete attribute level. Firstly, complete attribute level can be reduced using the heuristic attribute reduction algorithm. Secondly, the incomplete attribute can be reduced according to its significance one by one. Lastly, the rules can be extracted rightly. The hierarchical reduction approach can not only decrease the uncertainty because of completeness process, but also not lead the information losing of decision system because of stratifying process.
Through further researches on inconsistent decision-making information system, the depending relationship between the conditional attributes equivalence class and the decision attributes equivalence class is described using inclusion degree. According to the diversity of inclusion degree, the algorithms of distribution reduction, maximum distribution reduction and assignment reduction are researched respectively. The rule reliability by distribute reducing is tantamount with original attribute set. The difficulty to discover hidden rule is overcome and the reliability of hidden rule is given via distributing reduces to inconsistent information system. The hidden rules discovery can relax the conflict between great data size and necessitous knowledge. At the same time, the optimum choice of incomplete fault diagnosis system is offered using the maximum distribution reduction method.
Based on the decomposing strategy of equipment’s structure, the characteristic parameter space and the fault space split multi-subspace can reduced the dimension of input data of reasoning model. The intelligent diagnosis reasoning model is studied using information fuse and the ANN&FR parallel-series hybrid reasoning model by way of changing single-ANN for multi-ANN parallel integration is put up forward also. The learning speed can be improved effectively and the error concussion can be reduced and even be eliminated by adopting improved BP learning algorithm to learn each subnet. The diagnosis weight matrix can be self-adjusted in the process of fuzzy reasoning so as to increase the accuracy of fault diagnosis. At last, the parallel subnet composition principle and the structure optimization means of BP NN are discussed.
Based on analyzing the system requirements of remote intelligent fault diagnosis of loader, the general structure, functional and calculating model of fault diagnosis system of loader is proposed and realized. The practical results show this system can adapt the request of different consumers, structure reasonably, work steadily and practicability well.
引文
[1]吴今培,肖建华.智能故障诊断与专家系统.北京:科学出版社,1997
[2]虞和济.故障诊断的基本原理.北京:冶金工业出版社,1988
[3]吴今培.智能故障诊断技术的发展和展望.振动、测试与诊断,1999,19(2):79-86
[4]蔡卫峰.动态系统故障诊断技术研究进展与展望.计算机测量与控制, 2002,10(12): 775-777,781
[5]王道平,张义忠.故障智能诊断系统的理论与方法.北京:冶金工业出版社,2001
[6]杨叔子,郑小军.人工智能在机械设备诊断中的应用.机械工程,1987,23(4):10-15
[7]屈梁生,何正嘉.机械故障诊断学.上海:上海科学技术出版社,1996
[8] Liu Zhangseng. Machine Learning Method Applied to Acquire Knowledge of a Fault Diagnostic Expert System of Rotating Machineries. Journal of Harbin Institute of Technology, 1996,3(1):14-18
[9] E.A.Mohamed, A.Y.Abdelaziz, A.S.Mostafa.A Neural Network-based Scheme for Fault Diagnosis of Power Transformers. Electric Power Systems Research, 2005, 75: 29-39
[10] Enrique E.T., Demetrio Humana,Samuel Franco et al.Fault Diagnosis for a MSF Using Neural Networks.Desalination,2002,152:215-222
[11] Sheng Yung Chang, Cheng Ren Lin, Chuei Tin Chang.A Fuzzy Diagnosis Approach Using Dynamic Fault Trees. Chemical Engineering Science, 2002, 57: 2971-2985
[12] L.A.Zadeh. Fuzzy Sets.Information and Controller, 1965, 8:338-353
[13] Z.Pawlak. Rough Sets.Internation Journal of Computer and Information Sciences, 1982,11(15):341-356
[14] Kokar, M.M., J.A.Tomasik, J. Weyman. Formalizing Classes of Information Fusion Systems. Information Fusion, 2004.5(3):189-202
[15] Burak Ozyurt, Abraham Kandel. A Hybrid Hierarchical Neural Network-fuzzy Expert System Approach to Chemical Process Fault Diagnosis.Fuzzy Sets and Systems,1 996,83:11-25
[16] D.Richards, P.Compton. Alternative Verification and Validation Technique for an Alternative Knowledge Representation and Acquisition Technique. Knowledge–based System, 1999,12(1):55-73
[17]杨叔子.基于知识的诊断推理.北京:清华大学出版社,1993
[18]殷国富.工程专家系统技术及其应用.成都:成都科技大学出版社,1993
[19]张祖明.人工智能的前沿----专家系统的发展综述.北京印刷学院学报, 1995, 18(3):36-43
[20] L.科恩著,白居宪译.时频分析:理论与应用.西安:西安交通大学出版社,1998
[21] B.A.Paya, I.Esat, M.N.Badi. Artificial Neural Net-work based Fault Diagnosis of Rotating Machinery Using Wavelet Transforms as a Preprocessor. Mechanical systems and signal processing,1997,11(5):751-765
[22] Michele Basseville. Detecting Changes in Signals and Systems--A suevey. Automatica. 1998,24(3):306-329
[23] Oppenheim A.V, Lin J.S. The Importance of Plase in Signals. Proceedings of IEEE.1981, 69(5):529-541
[24]黄文虎,夏松波等.设备故障诊断原理、技术与应用.北京:科学出版社,1996
[25]王耀才.智能故障诊断技术的现状与展望.徐州建筑职业技术学院学报,2003, 18(3):37-39
[26]何岭松.设备故障诊断中信号的时频分析理论与技术的研究.华中理工大学博士学位论文,1993
[27]李洪兴,汪培庄.模糊数学.北京:国防工业出版社,1994
[28]姚敏.计算机模糊信息处理技术.上海:上海科学技术文献出版社,1999
[29]陈守煜.工程模糊集理论与应用.北京:国防工业出版社,1998
[30] M.Ulieru, R.ISermann. Fuzzy-logic based diagnostic model, Fuzzy Sets and Systems,1993,58:78-85
[31]李书明,曹林宁,沈祖诒.水轮发电机组微机电液调节系统故障模糊诊断方法.水利水电技术,2006,37(9):58-60
[32]张彼德,陈光禹.汽轮发电机组振动多故障诊断的遗传算法研究.汽轮机技术,2006, 48(4):278-280
[33]朱志宇,张冰,刘维亭.模糊支持向量机在船舶柴油机故障诊断中的应用.中国造船,2006,47(3):64-69
[34]何跃英,王荣汉.基于模糊理论的电力设备故障诊断专家系统.电工技术学报,1994, 3:43-46
[35] K.Kodera, R.Gendrin, C.D.Villedary. Analysis of Time-arying Signals with Small BT Values. IEEE Trans, ASSP, 1986,34:64-76
[36]施鸿宝.神经网络及其应用.西安:西安交通大学出版社,1993
[37]沈清,胡守仁.神经网络应用技术.长沙:国防科技大学出版社,1993
[38] R.Isermann. Fault Diagnosis of Machine via Parameter Estimation and Knowledge Processing-Tutorial paper.Automatica, 1993,29(4):815-835
[39] Benly Nevada Co.. Monitoring System: Engineer Assist Software, 1993
[40] R.Rengaswamy, Venkat V..A Syntactic Pattern-recognition Approach for ProcessMonitoring Fault Diagnosis.Engng. Applic. Artif, Intell., 1995,8(1):35-51
[41]夏卫星.BP网络在内燃机故障诊断中的应用.机械,2001(6):17-19
[42]张炜,张优云,战仁军等.旋转机械故障诊断中的神经网络改进算法研究.振动工程学报,1996(1):31-37
[43]胡凯成,孔凡让,李川奇.神经网络诊断模型的隐层压缩算法.振动工程学报, 1997(3):356-361
[44]彭文季,罗兴崎,赵道利.基于频谱法与径向基函数网络的水电机组振动故障诊断.中国电机工程学报,2006,26(9):154-158
[45] Jonas S.L.. Nonlinear Black-box Modeling in System Identification a Unified Overview. IFAC symposium on System identification, 1994
[46] Z.Pawlak. Rough Set Theory and its Application to Data Analysis. Cybernetics and Systems, 1998,9:661-688
[47] Leung Wong Man, Lam Wai, Sak Leung Kwong. Discovery Knowledge from Medical Databases Using Evolutionary Algorithms. IEEE Engineering in Medicine and Biology. 2000,19(4):45-55
[48] J.W.Grzymala Busse, Ming Fu, A Comparison of Several Approaches to Missing Attribute Values in Data Mining. Thr Second International Conference on Rough Sets and Current Trends in Computing, Banff,Canada,2000,340-347
[49] Tong Lingyun, An Liping.Incremental Learning of Decision Rules based on Rough Set Theory.Proceedings of the 4th World Congress on Intelligent Control and Automation,2002,Shanghai, China,420-425
[50] Yanyi Yang, T.C.Chiam. Rule Discovery based on Rough Set Theory. IEEE Power Engineering Society Winter Meeting, 2000:11-16
[51] Jung-heum Yon, Seung-moo Yang, Hong-tae Jeon. Structure Optimization of Fuzzy-Neural Network Using Rough Set Theory. IEEE International Fuzzy Systems Conference Proceedings,1999, Seoul, Korea,1666-1670
[52] Skowron A., Rauszer C. The Discernibility Matrices and Functions in Information Systems, In: Slowinski, R.(ed.), Intelligent Decision Support:Handbook of Applications and Advances of the Rough Sets Theory,1991:331-362
[53]叶东毅,陈昭炯.一个新的差别矩阵及其求核方法.电子学报,2002,30(7):1086-1089
[54]王兵,陈善本.一种基于差别矩阵的属性约简完备算法,上海交通大学学报, 2004, 38(1):43-47
[55] Hu X. Knowledge Discovery in Databases: an Attribute-oriented Rough Set Approach. Doctoral dissertation University of Regina, Canada, 1995
[56] Bjorvand A T. "Rough Enough"-A System Supporting the Rough Sets Approach. Http://home.sn.no/~torvill
[57] Staal V., Aleksander. Minimal Approximate Hitting Sets and Rule Templates, Internatinal Journal of Approximate Reasoning, 2000, 25:123-143
[58]苗夺谦,王珏.粗糙集理论中概念与运算的信息表示.软件学报, 1999, 10(2): 113-116
[59]王国胤,于洪,杨大春.基于条件信息熵的决策表约简.计算机学报, 2002, 25(7): 759-766
[60] Hong Yu, Guoyin Wang, Dachun Yang, Zhongfu Wu. Knowledge Reduction Algorithms based on Rough- set and Conditional Information Entropy, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV,Belur V.Dasarathy, Editor, Proceedings of SPIE Vol. 4730, 2002: 422-431
[61] G. Y. Wang. Algebra View and Information View of Rough Sets Theory, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, Belur V. Dasarathy, Editor, Proceedings of SPIE Vol. 4384,2001:200-207
[62] Wang Jue, Wang Ju. A kind of Reduction Algorithms based on Discernibility Matrix: The Ordered Attributes Method. Computer and Technology, 2001,16(6):489-504
[63] Su-Qing Han, Jue Wang. Reduct and Attribute Order. Journal of Computer Science and Technology, 2004, 19(4):1429-449
[64] Kryszkiewicz M. Rybinski H.Finding Reducts in Composed Information Systems. In: Ziarko W P (Ed.), Proceedings of RSKD'93.London: Springer-Verlag, 261-273
[65] Bazan J G, Skowron A, Synak P. Dynamic Reducts as A Tool for Extraction Laws from Decisions Tables, In: Ras ZZ W, Zemankiva M (Ed.), Methodologies for Intelligent Systems, Berlin: Springer-Verlag, 346-355
[66]米据生,吴伟志,张文修.基于变精度粗糙集理论的知识约简方法,系统工程理论与实践,2004,24(1):76-82
[67] Sai Ying, Y. Y Yao. Analyzing and Mining Ordered Information Tables.Journal of Computer&Technology, 2003, 18(6):771-779
[68]陈德刚,刘民,吴澄等.模糊信息系统的代数结构及其约简.清华大学学报,2003, 43(9):1233-1235, 1264
[69]李旭,刘辉林,徐心和.故障诊断专家系统的归纳学习方法.小型微型计算机系统,1997,18(6):70-72
[70]蔡自兴.人工智能及其应用.北京:清华大学出版社,1996
[71] Special Issue on Data Fusion. Proceedings of the IEEE, 1997,85(1):1-35
[72] Llinas J, Waltz E. Multisensor Data Fusion.Boston:Artech House,1990
[73] Gao Yang, Durrant-Whyte. Mufti-sensor Fault Detection and Diagnosis Using Combined Qualitative and Quantitative Techniques. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 1994.10:43-50
[74] Essawy M.A. Wavelet-Whyte. Multi-sensor Fault Detection and Using Combined Qualitative and Quantitative Techniques. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 1994,10:43-50
[75] Combastel Ch., Gentil,S..Symbolic Reasoning Approach to Fault Detection and Isolation Net. Proceedings of the SICE Annual Conference, 1999,7:893-896
[76] Liang Hao. Small Target Detection in Multisensor System based on Dempster-Shafter Evidence Theory. The International Society for Optical Engineering, 2001.9: 272-279
[77]马健仓,罗磊.小波-人工神经网络信息融合故障诊断方法.中国机械工程,1997, 8(4):38-40
[78]何永勇,钟秉林,黄仁.故障多征兆域一致性诊断策略的研究.振动工程学报, 1999(4):447-453
[79] Zhao Fang, Xie Youbai, Bai Ziyou.Characteristics and Inforniation Fusion of Multi-technique Integration for Oil Analysis. Mocaxue Xuebao/Tribology, 1998,1: 48-52
[80]严新平,谢友柏,萧汉梁.摩擦学故障种类诊断的D-S信息融合研究.摩擦学学报, 1999(6):145-150
[81]王江萍.基于多传感器融合信息的故障诊断.机械科学与技术,2000,19(11):950-953
[82]王耀南,李树涛.多传感器信息融合及其应用综述.控制与决策,2001,16(5):518-522
[83]胡运权,基于事例推理的智能模型构建研究,哈尔滨工业大学,博士学位论文,2000
[84]王道平,冯振声,何海燕.故障诊断专家系统中不确定性问题.计算机工程与应用, 1998,34(12):33-35
[85]陈湘晖,朱善君,吉吟东,李永敏.扩展的粗糙集模型及其不确定性量度.清华大学学报(自然科学版),2002,42(1): 35-38
[86]李凡.人工智能中不确定性.北京:气象出版社,1992
[87]张毅,安居白.Tickcon专家系统中的不确定性问题的研究.2002, 28(10):130-131
[88]李军旗,阎明印,史铁林.诊断专家系统的不确定性问题研究.华中理工大学学报, 1994,22(7):19-21
[89] Kryszkiewicz M..Rough Set Approach to Incomplete Information Systems. Information Sciences,1998,112(124):39-49
[90] Kryszkiewicz M..Generation of Rules from Incomplete Information Systems. In: Henryk Jan Komorowski, Jan M. Zytkow (eds.), Proc of the 1 St European Sympon on Principles of Data Mining and Knowledge Discovery. Berlin: Springer-Verlag, 1997:156-166
[91] Kryszkiewicz M..Rules in Incomplete Information Systems.Information Sciences,1999, 113, 271-292
[92] Stefanowski J, Tsoukias A.On the Extension of Rough Sets under Incomplete Information. Proceeding of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. Yamaguchi: Physica-Verlag, 1999, 73-81
[93]王国胤.Rough集理论在不完备信息系统中的扩充.计算机研究与发展, 2002, 39(10): 1238-1243
[94]赵卫东,曹文彬,戴伟辉.不完全信息下的粗集拓展.系统工程学报, 2002, 17(6): 481-485
[95]张宏宇,梁吉业.不完备信息系统下的变精度粗糙集模型及其知识约简算法.计算机科学,2003,30(4):153-155
[96]刘富春.不完备信息系统中粗糙集理论的扩充.计算机与现代化,2005,(6):1-3, 6
[97]黄斌,周献中,胡作进.不完备信息系统中一个变精度粗糙集模型.中北大学学报(自然科学版),2006,27(4):283-287
[98] Tzung-Pei Hong, Learning Rules from Incomplete Training Examples by Rough Sets.Expert Systems with Applications, 2002, 22, 285-293
[99] Yee Leung.Maximal Consistent Block Techique for Rule Acquisition in Incomplete Information Systems, Information Sciences, 2003,153:85-106
[100]何明,傅向华,马兆丰.基于不完备信息系统的Rough Set决策规则提取方法.计算机应用,2003,23(11):6-8
[101]程玉胜,胡学刚,江效尧.不完备信息系统的属性约简方法研究.计算机工程与应用, 2004, (1):68-70
[102]孙惠琴,熊璋,赵晓庆.不完备知识系统非对称相似关系的最小简式.计算机工程与设计,2005,26(2):436-438
[103]贺毅,蔡之华,毕进军.一种从不完备数据集中发现规则的RS方法.计算机工程, 2004, 30(8):162-163
[104]黄兵,周献中.不完备信息系统分配约简与规则提取的矩阵算法.计算机工程, 2005, 31(17):20-22
[105]黄兵,周献中.不一致决策表的分配序约简.南京理工大学学报(自然科学版), 2005, 29(3):360-362
[106] Lingras,P.J. Plausibilistic Rule Extraction from Incomplete Databases using Non-transitive Rough Set Model, Proceedings of the twenty-third Computer Science Conference Workshop on Rough Sets and Database Mining. 1995
[107] Lingras, P.J.,Yao Y.Y.. Data Mining using Extension of the Rough Set Model. Journal of the American Society for Information Science.1998, 49(5): 415-422
[108] Quinlan, J.R. Induction of Decision Tree, Machine Learning, 1989, 1:81-106
[109] Chmielewski, M.R., Grzymala-Busse, J.W.. The Rule Induction System LERS- A version for personal computers, Found Computer Decision Science 18:181-212
[110] WU Q., Suetens, P., Oosterlinck. A Integration of Heuristic and Bayesian Approaches in a Pattern-classification system. Knowledge Discovery in Database, AAAI/MIT press, 249-260
[111]卫金贸.数据挖掘若干问题的研究.华东理工大学博士学位论文,2001
[112]张伟,廖晓峰,吴中福.一种基于Rough集理论的不完备数据分析方法.模式识别与人工智能,2003,16(2):158-162
[113]张文修,吴伟志,梁吉业,李德玉.粗糙集理论与方法.北京:科学出版社,2001
[114] Greco S, MatarazzoB, Slowinski R. A new Rough Set Approach in Multicreteria and Multiattribute Classification. LNAI1424, RSCTC’98.Springer,1998
[115] Kryszkiewicz M. Comparative Studies of Alternative Type of Knowledge Reduction in Inconsistent Systems. International Journal of Intelligent Systems, 2001,16: 105-120
[116]张文修,米据生,吴伟志.不协调目标信息系统的知识约简.计算机学报, 2003, 26(1):12-18
[117]徐伟华,张文修.基于优势关系下不协调目标信息系统的知识约简.计算机科学,2006,33(2):182-184
[118] Quafatou M.α-RST: A Generalization of Rough Set Theory. Information Sciences, 2000,124:301-316
[119] Beynon M. Reducts within the Variable Precision Rough Sets Model: a Further Investigation. European Journal of Operational Research, 2001,134:592-605
[120]安芹力,李安平.不协调决策表的属性约简模型及规则提取.空军工程大学学报, 2005, 6(3):88-91
[121]杜卫锋,秦克云.不协调决策正域约简与其它约简的关系.海南师范学院学报(自然科学版),2005,18(1):8-11
[122] Z.Pawlak, Information System, Theoretical Foundation. Information System, 1981, 6(3): 205-218
[123] Z.Pawlak. Rough Set Approach to Multi-attribute Decision Analysis. European Journal of Operational Research, 1994,72: 443-459
[124] Z.Pawlak, Slowinski R.Rough Set Approach to Multi-attribute Decision Analysis. European Journal of Operational Research,1994,72:443-459
[125]王国胤.Rouugh集理论与知识获取.西安:西安交通大学出版社,2001
[126] Skowron A, Suraj Z. A Parallel Algorithm for Real-time Decision Making: A RoughSet Approach. Journal of Intelligent Information Systems, 1996, 7: 5-28
[127]张文修,仇国芳.基于粗糙集的不确定决策.北京:清华大学出版社,2005
[128] A.Skowron. The Discernibility Matrices and Functions in Information Systems. Handbook of Application and Advances of the Rough Set Theory. Kluwer Academic Publishers. 1992:331-363
[129] R.K. Aggarwal et al. Neural Network based on Adaptive Single-pole Autoreclosure Technique for CHV Transmission System. IEE proceedings-C, 1994,141(2):155-160
[130] H.S.Li, T.L.Shi, S.Z.Yang, et al. Internet-based Remote Diagnosis: Concept, System Architecture and Prototype. IEEE WCICA’2000(the 3rd World Congress on Intelligent Control & Automation), IEEE CN: 00EX393,2000.6:719-723
[131]郝忠孝.空值环境下的数据库导论.北京:机械工业出版社,1996
[132]乔斌,郑洪涛,郭智疆.针对信息系统不完备性的粗糙集分层递阶约简.电路与系统学报, 2001,6(2):78-82
[133]徐久成,沈钧毅,王国胤. Rough集之间的相似度量.计算机科学, 2003, 30(10): 55-57, 60
[134]翟敬梅,蒋梁中,谢存禧.基于相似关系的Rough集决策优化模型的研究.计算机科学,2002,29(9):67-69
[135] Aleksander Ohrm. ROSETTA Technical Reference Manual. Norwegian University of Science and Technology, 1999
[136] J. C. Platt. Fast Training of SVMs Using Sequential Minimal Optimization. MIT Press, 1998:185–208.
[137] Herzenberg L A, Black S J. Regulatory Circuits and Antibody Response. Europeion Journal of Immunology,1980,10:1-11
[138]薛国良.信息熵.物理通报, 1988,1:38-40
[139] Hu X H.Cercone N. Learning in Relational Database: a Rough Set Approach. Computational Intelligence. 1995, 11(2), 323-338
[140]王珏.Rough Set约简与数据浓缩.高技术通讯,1997,11(7):40-45
[141]刘宗田.属性最小约简的增量式算法.电子学报,1999,11(27):96-98
[142]张学民,安利平.动态数据库规则获取和更新的粗糙集方法.天津大学学报, 2001, 34(4):529-532
[143]安利平,吴育华,仝凌云.增量式获取规则的粗糙集方法.南开大学学报,2003, 36(2): 98-103
[144]张钹,张铃.问题求解理论及应用.北京:清华大学出版社,1990
[145]乔斌.分层递阶约简算法.浙江大学博士学位论文,2003
[146]苗夺谦. Rough Set理论及其在机器学习中的应用研究.中国科学院自动化研究所博士学位论文,1997
[147] J.W. Guan, D.A. Bell. Rough Computational Method for Information Systems. Artificial Intelligence, 1998, 105: 77-103
[148] Horikawa S, Furuhashi T, Chikawa Y U. On Fuzzy Modeling Using Fuzzy Neural Network with Back-propagation Algorithm. IEEE Trans.NN, 1992,3(5):801-806
[149] Gupta M M, Rao D H. On the Principle of Fuzzy Neural Networks. Fuzzy sets and Systems, 1994,61:1-18
[150] Yager R R.Implementing Fuzzy Logic Controller Using a Neural Network Framework. Fuzzy sets and Systems, 1992,48:53-64
[151] Hernandez A I, Garrault G, Mora F et al. Multisensor Fusion for Atrial and Ventricular Activity Detection in Coronary Care Monitoring. IEEE Trans on Biomed Eng, 1999,46(10):1186-1190
[152]乔士东.反馈神经网络的结构设计、学习算法及其应用研究.国防科技大学博士学位论文,2005
[153] Seref Naci Engin, Janset Kuvulmaz, Vasfi Emre Omurlu,Modeling of a coupled industrial Tank System with ANFIS.Lecture Notes in Computer Science.2004.29: 804-812
[154] Lehman A V, Park E, Liao P. Factors Influencing Learning in Back-Propagation. Proceedings of the IEEE Inter Conf. on Neural Networks, 1988;(1):335-341
[155]焦李成.神经网络系统理论,西安:西安电子科技大学出版社,1996
[156]刘耦耕,贺素良.BP神经网络结构参数的计算机自动确定.计算机工程与应用2004, 40(13):72-76
[157]周斌,李玉梅等.神经网络内燃机排放模型学习样本的选定.西南交通大学学报, 2002, 37(6):659-663
[158]中国科学院.2001科学发展报告.北京:科学出版社,2001
[2]虞和济.故障诊断的基本原理.北京:冶金工业出版社,1988
[3]吴今培.智能故障诊断技术的发展和展望.振动、测试与诊断,1999,19(2):79-86
[4]蔡卫峰.动态系统故障诊断技术研究进展与展望.计算机测量与控制, 2002,10(12): 775-777,781
[5]王道平,张义忠.故障智能诊断系统的理论与方法.北京:冶金工业出版社,2001
[6]杨叔子,郑小军.人工智能在机械设备诊断中的应用.机械工程,1987,23(4):10-15
[7]屈梁生,何正嘉.机械故障诊断学.上海:上海科学技术出版社,1996
[8] Liu Zhangseng. Machine Learning Method Applied to Acquire Knowledge of a Fault Diagnostic Expert System of Rotating Machineries. Journal of Harbin Institute of Technology, 1996,3(1):14-18
[9] E.A.Mohamed, A.Y.Abdelaziz, A.S.Mostafa.A Neural Network-based Scheme for Fault Diagnosis of Power Transformers. Electric Power Systems Research, 2005, 75: 29-39
[10] Enrique E.T., Demetrio Humana,Samuel Franco et al.Fault Diagnosis for a MSF Using Neural Networks.Desalination,2002,152:215-222
[11] Sheng Yung Chang, Cheng Ren Lin, Chuei Tin Chang.A Fuzzy Diagnosis Approach Using Dynamic Fault Trees. Chemical Engineering Science, 2002, 57: 2971-2985
[12] L.A.Zadeh. Fuzzy Sets.Information and Controller, 1965, 8:338-353
[13] Z.Pawlak. Rough Sets.Internation Journal of Computer and Information Sciences, 1982,11(15):341-356
[14] Kokar, M.M., J.A.Tomasik, J. Weyman. Formalizing Classes of Information Fusion Systems. Information Fusion, 2004.5(3):189-202
[15] Burak Ozyurt, Abraham Kandel. A Hybrid Hierarchical Neural Network-fuzzy Expert System Approach to Chemical Process Fault Diagnosis.Fuzzy Sets and Systems,1 996,83:11-25
[16] D.Richards, P.Compton. Alternative Verification and Validation Technique for an Alternative Knowledge Representation and Acquisition Technique. Knowledge–based System, 1999,12(1):55-73
[17]杨叔子.基于知识的诊断推理.北京:清华大学出版社,1993
[18]殷国富.工程专家系统技术及其应用.成都:成都科技大学出版社,1993
[19]张祖明.人工智能的前沿----专家系统的发展综述.北京印刷学院学报, 1995, 18(3):36-43
[20] L.科恩著,白居宪译.时频分析:理论与应用.西安:西安交通大学出版社,1998
[21] B.A.Paya, I.Esat, M.N.Badi. Artificial Neural Net-work based Fault Diagnosis of Rotating Machinery Using Wavelet Transforms as a Preprocessor. Mechanical systems and signal processing,1997,11(5):751-765
[22] Michele Basseville. Detecting Changes in Signals and Systems--A suevey. Automatica. 1998,24(3):306-329
[23] Oppenheim A.V, Lin J.S. The Importance of Plase in Signals. Proceedings of IEEE.1981, 69(5):529-541
[24]黄文虎,夏松波等.设备故障诊断原理、技术与应用.北京:科学出版社,1996
[25]王耀才.智能故障诊断技术的现状与展望.徐州建筑职业技术学院学报,2003, 18(3):37-39
[26]何岭松.设备故障诊断中信号的时频分析理论与技术的研究.华中理工大学博士学位论文,1993
[27]李洪兴,汪培庄.模糊数学.北京:国防工业出版社,1994
[28]姚敏.计算机模糊信息处理技术.上海:上海科学技术文献出版社,1999
[29]陈守煜.工程模糊集理论与应用.北京:国防工业出版社,1998
[30] M.Ulieru, R.ISermann. Fuzzy-logic based diagnostic model, Fuzzy Sets and Systems,1993,58:78-85
[31]李书明,曹林宁,沈祖诒.水轮发电机组微机电液调节系统故障模糊诊断方法.水利水电技术,2006,37(9):58-60
[32]张彼德,陈光禹.汽轮发电机组振动多故障诊断的遗传算法研究.汽轮机技术,2006, 48(4):278-280
[33]朱志宇,张冰,刘维亭.模糊支持向量机在船舶柴油机故障诊断中的应用.中国造船,2006,47(3):64-69
[34]何跃英,王荣汉.基于模糊理论的电力设备故障诊断专家系统.电工技术学报,1994, 3:43-46
[35] K.Kodera, R.Gendrin, C.D.Villedary. Analysis of Time-arying Signals with Small BT Values. IEEE Trans, ASSP, 1986,34:64-76
[36]施鸿宝.神经网络及其应用.西安:西安交通大学出版社,1993
[37]沈清,胡守仁.神经网络应用技术.长沙:国防科技大学出版社,1993
[38] R.Isermann. Fault Diagnosis of Machine via Parameter Estimation and Knowledge Processing-Tutorial paper.Automatica, 1993,29(4):815-835
[39] Benly Nevada Co.. Monitoring System: Engineer Assist Software, 1993
[40] R.Rengaswamy, Venkat V..A Syntactic Pattern-recognition Approach for ProcessMonitoring Fault Diagnosis.Engng. Applic. Artif, Intell., 1995,8(1):35-51
[41]夏卫星.BP网络在内燃机故障诊断中的应用.机械,2001(6):17-19
[42]张炜,张优云,战仁军等.旋转机械故障诊断中的神经网络改进算法研究.振动工程学报,1996(1):31-37
[43]胡凯成,孔凡让,李川奇.神经网络诊断模型的隐层压缩算法.振动工程学报, 1997(3):356-361
[44]彭文季,罗兴崎,赵道利.基于频谱法与径向基函数网络的水电机组振动故障诊断.中国电机工程学报,2006,26(9):154-158
[45] Jonas S.L.. Nonlinear Black-box Modeling in System Identification a Unified Overview. IFAC symposium on System identification, 1994
[46] Z.Pawlak. Rough Set Theory and its Application to Data Analysis. Cybernetics and Systems, 1998,9:661-688
[47] Leung Wong Man, Lam Wai, Sak Leung Kwong. Discovery Knowledge from Medical Databases Using Evolutionary Algorithms. IEEE Engineering in Medicine and Biology. 2000,19(4):45-55
[48] J.W.Grzymala Busse, Ming Fu, A Comparison of Several Approaches to Missing Attribute Values in Data Mining. Thr Second International Conference on Rough Sets and Current Trends in Computing, Banff,Canada,2000,340-347
[49] Tong Lingyun, An Liping.Incremental Learning of Decision Rules based on Rough Set Theory.Proceedings of the 4th World Congress on Intelligent Control and Automation,2002,Shanghai, China,420-425
[50] Yanyi Yang, T.C.Chiam. Rule Discovery based on Rough Set Theory. IEEE Power Engineering Society Winter Meeting, 2000:11-16
[51] Jung-heum Yon, Seung-moo Yang, Hong-tae Jeon. Structure Optimization of Fuzzy-Neural Network Using Rough Set Theory. IEEE International Fuzzy Systems Conference Proceedings,1999, Seoul, Korea,1666-1670
[52] Skowron A., Rauszer C. The Discernibility Matrices and Functions in Information Systems, In: Slowinski, R.(ed.), Intelligent Decision Support:Handbook of Applications and Advances of the Rough Sets Theory,1991:331-362
[53]叶东毅,陈昭炯.一个新的差别矩阵及其求核方法.电子学报,2002,30(7):1086-1089
[54]王兵,陈善本.一种基于差别矩阵的属性约简完备算法,上海交通大学学报, 2004, 38(1):43-47
[55] Hu X. Knowledge Discovery in Databases: an Attribute-oriented Rough Set Approach. Doctoral dissertation University of Regina, Canada, 1995
[56] Bjorvand A T. "Rough Enough"-A System Supporting the Rough Sets Approach. Http://home.sn.no/~torvill
[57] Staal V., Aleksander. Minimal Approximate Hitting Sets and Rule Templates, Internatinal Journal of Approximate Reasoning, 2000, 25:123-143
[58]苗夺谦,王珏.粗糙集理论中概念与运算的信息表示.软件学报, 1999, 10(2): 113-116
[59]王国胤,于洪,杨大春.基于条件信息熵的决策表约简.计算机学报, 2002, 25(7): 759-766
[60] Hong Yu, Guoyin Wang, Dachun Yang, Zhongfu Wu. Knowledge Reduction Algorithms based on Rough- set and Conditional Information Entropy, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV,Belur V.Dasarathy, Editor, Proceedings of SPIE Vol. 4730, 2002: 422-431
[61] G. Y. Wang. Algebra View and Information View of Rough Sets Theory, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, Belur V. Dasarathy, Editor, Proceedings of SPIE Vol. 4384,2001:200-207
[62] Wang Jue, Wang Ju. A kind of Reduction Algorithms based on Discernibility Matrix: The Ordered Attributes Method. Computer and Technology, 2001,16(6):489-504
[63] Su-Qing Han, Jue Wang. Reduct and Attribute Order. Journal of Computer Science and Technology, 2004, 19(4):1429-449
[64] Kryszkiewicz M. Rybinski H.Finding Reducts in Composed Information Systems. In: Ziarko W P (Ed.), Proceedings of RSKD'93.London: Springer-Verlag, 261-273
[65] Bazan J G, Skowron A, Synak P. Dynamic Reducts as A Tool for Extraction Laws from Decisions Tables, In: Ras ZZ W, Zemankiva M (Ed.), Methodologies for Intelligent Systems, Berlin: Springer-Verlag, 346-355
[66]米据生,吴伟志,张文修.基于变精度粗糙集理论的知识约简方法,系统工程理论与实践,2004,24(1):76-82
[67] Sai Ying, Y. Y Yao. Analyzing and Mining Ordered Information Tables.Journal of Computer&Technology, 2003, 18(6):771-779
[68]陈德刚,刘民,吴澄等.模糊信息系统的代数结构及其约简.清华大学学报,2003, 43(9):1233-1235, 1264
[69]李旭,刘辉林,徐心和.故障诊断专家系统的归纳学习方法.小型微型计算机系统,1997,18(6):70-72
[70]蔡自兴.人工智能及其应用.北京:清华大学出版社,1996
[71] Special Issue on Data Fusion. Proceedings of the IEEE, 1997,85(1):1-35
[72] Llinas J, Waltz E. Multisensor Data Fusion.Boston:Artech House,1990
[73] Gao Yang, Durrant-Whyte. Mufti-sensor Fault Detection and Diagnosis Using Combined Qualitative and Quantitative Techniques. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 1994.10:43-50
[74] Essawy M.A. Wavelet-Whyte. Multi-sensor Fault Detection and Using Combined Qualitative and Quantitative Techniques. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 1994,10:43-50
[75] Combastel Ch., Gentil,S..Symbolic Reasoning Approach to Fault Detection and Isolation Net. Proceedings of the SICE Annual Conference, 1999,7:893-896
[76] Liang Hao. Small Target Detection in Multisensor System based on Dempster-Shafter Evidence Theory. The International Society for Optical Engineering, 2001.9: 272-279
[77]马健仓,罗磊.小波-人工神经网络信息融合故障诊断方法.中国机械工程,1997, 8(4):38-40
[78]何永勇,钟秉林,黄仁.故障多征兆域一致性诊断策略的研究.振动工程学报, 1999(4):447-453
[79] Zhao Fang, Xie Youbai, Bai Ziyou.Characteristics and Inforniation Fusion of Multi-technique Integration for Oil Analysis. Mocaxue Xuebao/Tribology, 1998,1: 48-52
[80]严新平,谢友柏,萧汉梁.摩擦学故障种类诊断的D-S信息融合研究.摩擦学学报, 1999(6):145-150
[81]王江萍.基于多传感器融合信息的故障诊断.机械科学与技术,2000,19(11):950-953
[82]王耀南,李树涛.多传感器信息融合及其应用综述.控制与决策,2001,16(5):518-522
[83]胡运权,基于事例推理的智能模型构建研究,哈尔滨工业大学,博士学位论文,2000
[84]王道平,冯振声,何海燕.故障诊断专家系统中不确定性问题.计算机工程与应用, 1998,34(12):33-35
[85]陈湘晖,朱善君,吉吟东,李永敏.扩展的粗糙集模型及其不确定性量度.清华大学学报(自然科学版),2002,42(1): 35-38
[86]李凡.人工智能中不确定性.北京:气象出版社,1992
[87]张毅,安居白.Tickcon专家系统中的不确定性问题的研究.2002, 28(10):130-131
[88]李军旗,阎明印,史铁林.诊断专家系统的不确定性问题研究.华中理工大学学报, 1994,22(7):19-21
[89] Kryszkiewicz M..Rough Set Approach to Incomplete Information Systems. Information Sciences,1998,112(124):39-49
[90] Kryszkiewicz M..Generation of Rules from Incomplete Information Systems. In: Henryk Jan Komorowski, Jan M. Zytkow (eds.), Proc of the 1 St European Sympon on Principles of Data Mining and Knowledge Discovery. Berlin: Springer-Verlag, 1997:156-166
[91] Kryszkiewicz M..Rules in Incomplete Information Systems.Information Sciences,1999, 113, 271-292
[92] Stefanowski J, Tsoukias A.On the Extension of Rough Sets under Incomplete Information. Proceeding of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. Yamaguchi: Physica-Verlag, 1999, 73-81
[93]王国胤.Rough集理论在不完备信息系统中的扩充.计算机研究与发展, 2002, 39(10): 1238-1243
[94]赵卫东,曹文彬,戴伟辉.不完全信息下的粗集拓展.系统工程学报, 2002, 17(6): 481-485
[95]张宏宇,梁吉业.不完备信息系统下的变精度粗糙集模型及其知识约简算法.计算机科学,2003,30(4):153-155
[96]刘富春.不完备信息系统中粗糙集理论的扩充.计算机与现代化,2005,(6):1-3, 6
[97]黄斌,周献中,胡作进.不完备信息系统中一个变精度粗糙集模型.中北大学学报(自然科学版),2006,27(4):283-287
[98] Tzung-Pei Hong, Learning Rules from Incomplete Training Examples by Rough Sets.Expert Systems with Applications, 2002, 22, 285-293
[99] Yee Leung.Maximal Consistent Block Techique for Rule Acquisition in Incomplete Information Systems, Information Sciences, 2003,153:85-106
[100]何明,傅向华,马兆丰.基于不完备信息系统的Rough Set决策规则提取方法.计算机应用,2003,23(11):6-8
[101]程玉胜,胡学刚,江效尧.不完备信息系统的属性约简方法研究.计算机工程与应用, 2004, (1):68-70
[102]孙惠琴,熊璋,赵晓庆.不完备知识系统非对称相似关系的最小简式.计算机工程与设计,2005,26(2):436-438
[103]贺毅,蔡之华,毕进军.一种从不完备数据集中发现规则的RS方法.计算机工程, 2004, 30(8):162-163
[104]黄兵,周献中.不完备信息系统分配约简与规则提取的矩阵算法.计算机工程, 2005, 31(17):20-22
[105]黄兵,周献中.不一致决策表的分配序约简.南京理工大学学报(自然科学版), 2005, 29(3):360-362
[106] Lingras,P.J. Plausibilistic Rule Extraction from Incomplete Databases using Non-transitive Rough Set Model, Proceedings of the twenty-third Computer Science Conference Workshop on Rough Sets and Database Mining. 1995
[107] Lingras, P.J.,Yao Y.Y.. Data Mining using Extension of the Rough Set Model. Journal of the American Society for Information Science.1998, 49(5): 415-422
[108] Quinlan, J.R. Induction of Decision Tree, Machine Learning, 1989, 1:81-106
[109] Chmielewski, M.R., Grzymala-Busse, J.W.. The Rule Induction System LERS- A version for personal computers, Found Computer Decision Science 18:181-212
[110] WU Q., Suetens, P., Oosterlinck. A Integration of Heuristic and Bayesian Approaches in a Pattern-classification system. Knowledge Discovery in Database, AAAI/MIT press, 249-260
[111]卫金贸.数据挖掘若干问题的研究.华东理工大学博士学位论文,2001
[112]张伟,廖晓峰,吴中福.一种基于Rough集理论的不完备数据分析方法.模式识别与人工智能,2003,16(2):158-162
[113]张文修,吴伟志,梁吉业,李德玉.粗糙集理论与方法.北京:科学出版社,2001
[114] Greco S, MatarazzoB, Slowinski R. A new Rough Set Approach in Multicreteria and Multiattribute Classification. LNAI1424, RSCTC’98.Springer,1998
[115] Kryszkiewicz M. Comparative Studies of Alternative Type of Knowledge Reduction in Inconsistent Systems. International Journal of Intelligent Systems, 2001,16: 105-120
[116]张文修,米据生,吴伟志.不协调目标信息系统的知识约简.计算机学报, 2003, 26(1):12-18
[117]徐伟华,张文修.基于优势关系下不协调目标信息系统的知识约简.计算机科学,2006,33(2):182-184
[118] Quafatou M.α-RST: A Generalization of Rough Set Theory. Information Sciences, 2000,124:301-316
[119] Beynon M. Reducts within the Variable Precision Rough Sets Model: a Further Investigation. European Journal of Operational Research, 2001,134:592-605
[120]安芹力,李安平.不协调决策表的属性约简模型及规则提取.空军工程大学学报, 2005, 6(3):88-91
[121]杜卫锋,秦克云.不协调决策正域约简与其它约简的关系.海南师范学院学报(自然科学版),2005,18(1):8-11
[122] Z.Pawlak, Information System, Theoretical Foundation. Information System, 1981, 6(3): 205-218
[123] Z.Pawlak. Rough Set Approach to Multi-attribute Decision Analysis. European Journal of Operational Research, 1994,72: 443-459
[124] Z.Pawlak, Slowinski R.Rough Set Approach to Multi-attribute Decision Analysis. European Journal of Operational Research,1994,72:443-459
[125]王国胤.Rouugh集理论与知识获取.西安:西安交通大学出版社,2001
[126] Skowron A, Suraj Z. A Parallel Algorithm for Real-time Decision Making: A RoughSet Approach. Journal of Intelligent Information Systems, 1996, 7: 5-28
[127]张文修,仇国芳.基于粗糙集的不确定决策.北京:清华大学出版社,2005
[128] A.Skowron. The Discernibility Matrices and Functions in Information Systems. Handbook of Application and Advances of the Rough Set Theory. Kluwer Academic Publishers. 1992:331-363
[129] R.K. Aggarwal et al. Neural Network based on Adaptive Single-pole Autoreclosure Technique for CHV Transmission System. IEE proceedings-C, 1994,141(2):155-160
[130] H.S.Li, T.L.Shi, S.Z.Yang, et al. Internet-based Remote Diagnosis: Concept, System Architecture and Prototype. IEEE WCICA’2000(the 3rd World Congress on Intelligent Control & Automation), IEEE CN: 00EX393,2000.6:719-723
[131]郝忠孝.空值环境下的数据库导论.北京:机械工业出版社,1996
[132]乔斌,郑洪涛,郭智疆.针对信息系统不完备性的粗糙集分层递阶约简.电路与系统学报, 2001,6(2):78-82
[133]徐久成,沈钧毅,王国胤. Rough集之间的相似度量.计算机科学, 2003, 30(10): 55-57, 60
[134]翟敬梅,蒋梁中,谢存禧.基于相似关系的Rough集决策优化模型的研究.计算机科学,2002,29(9):67-69
[135] Aleksander Ohrm. ROSETTA Technical Reference Manual. Norwegian University of Science and Technology, 1999
[136] J. C. Platt. Fast Training of SVMs Using Sequential Minimal Optimization. MIT Press, 1998:185–208.
[137] Herzenberg L A, Black S J. Regulatory Circuits and Antibody Response. Europeion Journal of Immunology,1980,10:1-11
[138]薛国良.信息熵.物理通报, 1988,1:38-40
[139] Hu X H.Cercone N. Learning in Relational Database: a Rough Set Approach. Computational Intelligence. 1995, 11(2), 323-338
[140]王珏.Rough Set约简与数据浓缩.高技术通讯,1997,11(7):40-45
[141]刘宗田.属性最小约简的增量式算法.电子学报,1999,11(27):96-98
[142]张学民,安利平.动态数据库规则获取和更新的粗糙集方法.天津大学学报, 2001, 34(4):529-532
[143]安利平,吴育华,仝凌云.增量式获取规则的粗糙集方法.南开大学学报,2003, 36(2): 98-103
[144]张钹,张铃.问题求解理论及应用.北京:清华大学出版社,1990
[145]乔斌.分层递阶约简算法.浙江大学博士学位论文,2003
[146]苗夺谦. Rough Set理论及其在机器学习中的应用研究.中国科学院自动化研究所博士学位论文,1997
[147] J.W. Guan, D.A. Bell. Rough Computational Method for Information Systems. Artificial Intelligence, 1998, 105: 77-103
[148] Horikawa S, Furuhashi T, Chikawa Y U. On Fuzzy Modeling Using Fuzzy Neural Network with Back-propagation Algorithm. IEEE Trans.NN, 1992,3(5):801-806
[149] Gupta M M, Rao D H. On the Principle of Fuzzy Neural Networks. Fuzzy sets and Systems, 1994,61:1-18
[150] Yager R R.Implementing Fuzzy Logic Controller Using a Neural Network Framework. Fuzzy sets and Systems, 1992,48:53-64
[151] Hernandez A I, Garrault G, Mora F et al. Multisensor Fusion for Atrial and Ventricular Activity Detection in Coronary Care Monitoring. IEEE Trans on Biomed Eng, 1999,46(10):1186-1190
[152]乔士东.反馈神经网络的结构设计、学习算法及其应用研究.国防科技大学博士学位论文,2005
[153] Seref Naci Engin, Janset Kuvulmaz, Vasfi Emre Omurlu,Modeling of a coupled industrial Tank System with ANFIS.Lecture Notes in Computer Science.2004.29: 804-812
[154] Lehman A V, Park E, Liao P. Factors Influencing Learning in Back-Propagation. Proceedings of the IEEE Inter Conf. on Neural Networks, 1988;(1):335-341
[155]焦李成.神经网络系统理论,西安:西安电子科技大学出版社,1996
[156]刘耦耕,贺素良.BP神经网络结构参数的计算机自动确定.计算机工程与应用2004, 40(13):72-76
[157]周斌,李玉梅等.神经网络内燃机排放模型学习样本的选定.西南交通大学学报, 2002, 37(6):659-663
[158]中国科学院.2001科学发展报告.北京:科学出版社,2001