面向中医辨证计算的粗糙集知识获取方法及其应用研究
|
摘要
随着科学技术的高速发展,智能信息处理已成为众多学科领域研究的热点。当前中医现代化的进展迫切需要先进的智能信息处理技术的支撑。中医诊断现代化无疑是中医现代化的重要方面。其中,中医智能诊断是中医诊断技术与智能信息处理技术相结合的较好切入点,其必须解决的核心问题和关键技术在于中医智能辨证。
早期的研究实践表明,中医智能辨证的关键环节在于知识的处理,包括知识的表示、获取、发现与利用等方面。其中所面临的许多问题与困难也是当前人工智能领域研究的热点与难点。基于软计算思想的一系列新型智能信息处理技术的兴起,为更好地解决这些问题与难点带来了机遇,而其自身也可从解决问题的过程中获得新的启迪,丰富其研究内容与成果。
论文研究并分析了软计算方法在中医辨证智能诊断领域中的研究现状、基本方法及面临的困难,对不确定性知识的表示及处理,归纳与模拟人类专家的经验并建立相应的信息模型,从大规模数据中获取或发现知识,以及计算大规模信息系统的属性约简等关键技术的研究作了有益的探索,旨在为中医辨证计算化的研究与实现提出新的思路、方法和技术,也为人工智能领域中相关的难点问题提出新的解决办法。
论文主要研究内容如下:
第一章首先阐述了论文研究的时代背景及学科交叉特色;然后指出了目前中医智能诊断研究面临的挑战与意义,以及软计算在中医辨证计算化研究中具有的独特作用;最后概述了粗糙集理论及应用的研究进展,着重分析了粗糙集在知识获取与属性约简两方面的研究内容与意义,以及粗糙集在中医智能诊断方面的初步应用与存在的问题。
第二章首先介绍了中医辨证的基本概念、辨证原理和辨证方法;然后分析了软计算在中医辨证智能诊断研究中的优势与难点;最后详细阐述了中医辨证智能诊断的软计算方法研究的进展。本章归纳总结了基于模糊集理论的中医辨证诊断方法和基于模糊集理论的中医证型的模糊聚类方法;分析了神经网络在中医辨证智能诊断中的应用研究现状、基本观点、一般方法、存在的问题、解决问题的思路,并介绍了基于神经网络的中医辨证智能诊断研究整体思路的初步设想和所做的相关研究工作;回顾并总结了粗糙集理论在中医辨证智能诊断中的一般步骤;概要介绍了当前多技术融合方面的相关研究工作与研究趋势。
针对知识获取这一智能系统开发的瓶颈问题,第三章和第四章分别针对人类专家的两种思维方式—“聚焦”和“层级聚类”—进行了深入探讨,发现已有的模拟这些思维方式的分类规则提取方法的局限性:它们在聚焦机制的排除过程和鉴别过程中都采用覆盖准则,导致其鉴别过程只能适用于在二者之间进行。为此,提出了改进办法。改进思路的基本出发点是:若在鉴别过程中采用精度准则,则可以使鉴别过程在多者之间进行,进一步地,还可以与属性约简方法相结合,消除冗余属性。第三章针对“聚焦”思维方式,提出了分类规则提取算法REFM;第四章针对“层级聚类”思维方式,提出了诊断规则提取算法REHC。
针对计算大型信息系统的所有属性约简(包括计算其所有最小属性约简)这一NP-hard问题,第五章首先考察了分辨函数的一系列等价形式;然后提出了约简分辨图的概念,以及深度优先搜索的三项原则:成员独占原则(MEP)、友人劝阻原则(FPP)、陌生人吸纳原则(SEP);进而阐述了基于约简分辨图计算属性约简的完整理论及算法CARRDG,并从理论上严密论证了算法CARRDG的高效性与完全性;最后用六种典型的UCI数据进行实验验证。UCI数据实验表明:对于多数信息系统,算法CARRDG计算所有属性约简的时间小于0.5秒;对于对象数达到20000的信息系统,算法CARRDG的剪枝率可达90%以上,且可在几分钟内计算出所有属性约简。算法CARRDG虽然是针对属性约简的计算问题提出的,但其实质上解决的是将合取范式快速转化为析取范式并进行简化的问题,因而具有广阔的应用空间。故第五章的理论价值不仅在于为计算所有属性约简(包括计算所有最小属性约简),提出了新的观点、思路、理论和方法,而且在于给出了采用基于约简分辨图的启发式搜索,解决逻辑表达式转化与简化中的组合爆炸问题的新思路。
第六章首先提出了学习型中医辨证诊疗系统的构想,分析了论文研究成果在此构想中的应用方式及意义;然后总结了论文的主要工作与创新点;最后阐明了目前研究工作中有待完善之处、存在的困难及未来的研究方向与前景。
With the rapid development of modern science and technology, intelligent information processing has become hot point in many research fields, thus the corresponding technologies have become the urgent supporting power for the modernization of traditional Chinese medicine (TCM). While the modernization of diagnosis in TCM is one of the important facets of the modernization of TCM, intelligent diagnosis in TCM appears to be the perfect research entry for combing intelligent technologies with diagnostic technologies in TCM, and its core problem and key technology lie in the intelligent syndrome differentiation in TCM (SDTCM)(syndrome differentiation is an unique concept of TCM).
Based on the earlier research activities, it has been emerged that the key point of intelligent diagnosis in TCM may be related to knowledge processing including knowledge representation, knowledge acquisition, knowledge discovery and knowledge utilization, etc., and the difficult problems occurred are always the important research topics in the area of artificial intelligence. Fortunately, a series of advanced intelligent technologies based on soft computing have brought great opportunity for solving these problems, and they, in return, also can attain new enlightenment and enrich their research content and harvest.
In this dissertation, the state-of-the-art of soft computing for the intelligent SDTCM as well as its fundamental methods and difficulties are studied and analyzed. The other research topics involve in representing uncertain knowledge, designing information model by concluding and simulating experiences of human experts, discovering knowledge from large data, and reducing large data, etc. The purpose of this dissertation is to provide some new ideals, methods and technologies for the realization of the computing in SDTCM, and to propose several new resolutions for the related hard problems in the area of artificial intelligence.
The main content is as follows.
In the first chapter, the background and the significance of the research are interpreted and the research activities and applications about rough set theory are reviewed. It is also pointed out that the critical point of the intelligent diagnosis in TCM is the realization of the computing of SDTCM, and the main corresponding problems lie in knowledge processing including knowledge acquisition, knowledge discovery and knowledge exploiting. As for the rough set theory, the state-of-the-art is outlined, and the research works based on it are analyzed involving knowledge acquisition, attribution reduct as well as the application in the intelligent SDTCM.
In the second chapter, first, the fundamental concepts, principles and methods of SDTCM are introduced. Second, the advantages and difficulties of soft computing in the research on the intelligent SDTCM are analyzed; Finally, the state-of-the-art of the intelligent SDTCM is interpreted in detail: 1) the diagnostic methods and clustering methods of syndromes for SDTCM based on fuzzy set theory are concluded ; 2) the stat-of-the-art, fundamental viewpoints, general methods, existing problems with corresponding resolution ideas of the applications based on neural network for SDTCM are analyzed, and our initial idea and research works for this area are introduced; 3) the general steps of the practical methods based on rough set theory for SDTCM are reviewed and concluded; 4) the research works for the fusion of multi-technology are also introduced in brief.
Aiming at the problem about knowledge acquisition, which is a neck problem when developing an intelligent system, in the third and fourth chapters, two kinds of human experts' thinking ways, the focusing mechanism and the hierarchical clustering mechanism, are respectively examined, and the limitations of the existed approaches to extracting classification rules by modeling these two thinking ways are carefully examined. The focusing mechanism is clearly represented by three ordered processes: exclusion process, discrimination process and combination process. One of the main limitations of the approaches existed is that they can only do discrimination between two classes, since they exploit coverage criteria in the discrimination process. The point departure for the improvement is: if exploiting accuracy criteria in the discrimination process, then the discrimination process may be done among many classes. Furthermore, they can be combined with the methods for computing attribute reducts. For the focusing mechanism, the algorithms REFM is proposed in the third chapter. For the hierarchical clustering mechanism, the algorithm REHC is proposed in the fourth chapter.
To challenge the NP-hard problem computing the total attribute reducts (including the total minimal attribute reducts) in large information systems, in the fifnth chapter, a series of equivalent form of discernibility function are examined; then an important and novel concept, i.e. reduct disernibility graph (RDG), is proposed; furthermore, the complete theory for computing attribute reducts based on RDG as well as the corresponding algorithm CARRDG are proposed and interpreted; the effectiveness and completeness of the algorithm CARRDG are also proved; at last the results of the experiment on six typical UCI data sets show: the algorithm CARRDG can compute total attribute reducts within 0.5 seconds for the general information systems, and within several minutes and more than 90% trim rate for the large information systems even with 20000 objects. It should be noted that although the algorithm CARRDG is proposed for the special problem, in fact it solves, however, the problem of the transformation and simplification of logic expressions from conjunctive normal form to disjunctive normal form, so it has wide variety of application fields, and it maight also open a new window for solving the combination exposition problems in the real-world problems.
In the sixth chapter, the tentative idea for the future structure of the SDTCM system with learning mechanism is proposed, and the application means and the significance of our research fruits in the structure are interpreted. At last, the main research work and the existing problems of this dissertation are concluded and the future of the research is also prospected.
早期的研究实践表明,中医智能辨证的关键环节在于知识的处理,包括知识的表示、获取、发现与利用等方面。其中所面临的许多问题与困难也是当前人工智能领域研究的热点与难点。基于软计算思想的一系列新型智能信息处理技术的兴起,为更好地解决这些问题与难点带来了机遇,而其自身也可从解决问题的过程中获得新的启迪,丰富其研究内容与成果。
论文研究并分析了软计算方法在中医辨证智能诊断领域中的研究现状、基本方法及面临的困难,对不确定性知识的表示及处理,归纳与模拟人类专家的经验并建立相应的信息模型,从大规模数据中获取或发现知识,以及计算大规模信息系统的属性约简等关键技术的研究作了有益的探索,旨在为中医辨证计算化的研究与实现提出新的思路、方法和技术,也为人工智能领域中相关的难点问题提出新的解决办法。
论文主要研究内容如下:
第一章首先阐述了论文研究的时代背景及学科交叉特色;然后指出了目前中医智能诊断研究面临的挑战与意义,以及软计算在中医辨证计算化研究中具有的独特作用;最后概述了粗糙集理论及应用的研究进展,着重分析了粗糙集在知识获取与属性约简两方面的研究内容与意义,以及粗糙集在中医智能诊断方面的初步应用与存在的问题。
第二章首先介绍了中医辨证的基本概念、辨证原理和辨证方法;然后分析了软计算在中医辨证智能诊断研究中的优势与难点;最后详细阐述了中医辨证智能诊断的软计算方法研究的进展。本章归纳总结了基于模糊集理论的中医辨证诊断方法和基于模糊集理论的中医证型的模糊聚类方法;分析了神经网络在中医辨证智能诊断中的应用研究现状、基本观点、一般方法、存在的问题、解决问题的思路,并介绍了基于神经网络的中医辨证智能诊断研究整体思路的初步设想和所做的相关研究工作;回顾并总结了粗糙集理论在中医辨证智能诊断中的一般步骤;概要介绍了当前多技术融合方面的相关研究工作与研究趋势。
针对知识获取这一智能系统开发的瓶颈问题,第三章和第四章分别针对人类专家的两种思维方式—“聚焦”和“层级聚类”—进行了深入探讨,发现已有的模拟这些思维方式的分类规则提取方法的局限性:它们在聚焦机制的排除过程和鉴别过程中都采用覆盖准则,导致其鉴别过程只能适用于在二者之间进行。为此,提出了改进办法。改进思路的基本出发点是:若在鉴别过程中采用精度准则,则可以使鉴别过程在多者之间进行,进一步地,还可以与属性约简方法相结合,消除冗余属性。第三章针对“聚焦”思维方式,提出了分类规则提取算法REFM;第四章针对“层级聚类”思维方式,提出了诊断规则提取算法REHC。
针对计算大型信息系统的所有属性约简(包括计算其所有最小属性约简)这一NP-hard问题,第五章首先考察了分辨函数的一系列等价形式;然后提出了约简分辨图的概念,以及深度优先搜索的三项原则:成员独占原则(MEP)、友人劝阻原则(FPP)、陌生人吸纳原则(SEP);进而阐述了基于约简分辨图计算属性约简的完整理论及算法CARRDG,并从理论上严密论证了算法CARRDG的高效性与完全性;最后用六种典型的UCI数据进行实验验证。UCI数据实验表明:对于多数信息系统,算法CARRDG计算所有属性约简的时间小于0.5秒;对于对象数达到20000的信息系统,算法CARRDG的剪枝率可达90%以上,且可在几分钟内计算出所有属性约简。算法CARRDG虽然是针对属性约简的计算问题提出的,但其实质上解决的是将合取范式快速转化为析取范式并进行简化的问题,因而具有广阔的应用空间。故第五章的理论价值不仅在于为计算所有属性约简(包括计算所有最小属性约简),提出了新的观点、思路、理论和方法,而且在于给出了采用基于约简分辨图的启发式搜索,解决逻辑表达式转化与简化中的组合爆炸问题的新思路。
第六章首先提出了学习型中医辨证诊疗系统的构想,分析了论文研究成果在此构想中的应用方式及意义;然后总结了论文的主要工作与创新点;最后阐明了目前研究工作中有待完善之处、存在的困难及未来的研究方向与前景。
With the rapid development of modern science and technology, intelligent information processing has become hot point in many research fields, thus the corresponding technologies have become the urgent supporting power for the modernization of traditional Chinese medicine (TCM). While the modernization of diagnosis in TCM is one of the important facets of the modernization of TCM, intelligent diagnosis in TCM appears to be the perfect research entry for combing intelligent technologies with diagnostic technologies in TCM, and its core problem and key technology lie in the intelligent syndrome differentiation in TCM (SDTCM)(syndrome differentiation is an unique concept of TCM).
Based on the earlier research activities, it has been emerged that the key point of intelligent diagnosis in TCM may be related to knowledge processing including knowledge representation, knowledge acquisition, knowledge discovery and knowledge utilization, etc., and the difficult problems occurred are always the important research topics in the area of artificial intelligence. Fortunately, a series of advanced intelligent technologies based on soft computing have brought great opportunity for solving these problems, and they, in return, also can attain new enlightenment and enrich their research content and harvest.
In this dissertation, the state-of-the-art of soft computing for the intelligent SDTCM as well as its fundamental methods and difficulties are studied and analyzed. The other research topics involve in representing uncertain knowledge, designing information model by concluding and simulating experiences of human experts, discovering knowledge from large data, and reducing large data, etc. The purpose of this dissertation is to provide some new ideals, methods and technologies for the realization of the computing in SDTCM, and to propose several new resolutions for the related hard problems in the area of artificial intelligence.
The main content is as follows.
In the first chapter, the background and the significance of the research are interpreted and the research activities and applications about rough set theory are reviewed. It is also pointed out that the critical point of the intelligent diagnosis in TCM is the realization of the computing of SDTCM, and the main corresponding problems lie in knowledge processing including knowledge acquisition, knowledge discovery and knowledge exploiting. As for the rough set theory, the state-of-the-art is outlined, and the research works based on it are analyzed involving knowledge acquisition, attribution reduct as well as the application in the intelligent SDTCM.
In the second chapter, first, the fundamental concepts, principles and methods of SDTCM are introduced. Second, the advantages and difficulties of soft computing in the research on the intelligent SDTCM are analyzed; Finally, the state-of-the-art of the intelligent SDTCM is interpreted in detail: 1) the diagnostic methods and clustering methods of syndromes for SDTCM based on fuzzy set theory are concluded ; 2) the stat-of-the-art, fundamental viewpoints, general methods, existing problems with corresponding resolution ideas of the applications based on neural network for SDTCM are analyzed, and our initial idea and research works for this area are introduced; 3) the general steps of the practical methods based on rough set theory for SDTCM are reviewed and concluded; 4) the research works for the fusion of multi-technology are also introduced in brief.
Aiming at the problem about knowledge acquisition, which is a neck problem when developing an intelligent system, in the third and fourth chapters, two kinds of human experts' thinking ways, the focusing mechanism and the hierarchical clustering mechanism, are respectively examined, and the limitations of the existed approaches to extracting classification rules by modeling these two thinking ways are carefully examined. The focusing mechanism is clearly represented by three ordered processes: exclusion process, discrimination process and combination process. One of the main limitations of the approaches existed is that they can only do discrimination between two classes, since they exploit coverage criteria in the discrimination process. The point departure for the improvement is: if exploiting accuracy criteria in the discrimination process, then the discrimination process may be done among many classes. Furthermore, they can be combined with the methods for computing attribute reducts. For the focusing mechanism, the algorithms REFM is proposed in the third chapter. For the hierarchical clustering mechanism, the algorithm REHC is proposed in the fourth chapter.
To challenge the NP-hard problem computing the total attribute reducts (including the total minimal attribute reducts) in large information systems, in the fifnth chapter, a series of equivalent form of discernibility function are examined; then an important and novel concept, i.e. reduct disernibility graph (RDG), is proposed; furthermore, the complete theory for computing attribute reducts based on RDG as well as the corresponding algorithm CARRDG are proposed and interpreted; the effectiveness and completeness of the algorithm CARRDG are also proved; at last the results of the experiment on six typical UCI data sets show: the algorithm CARRDG can compute total attribute reducts within 0.5 seconds for the general information systems, and within several minutes and more than 90% trim rate for the large information systems even with 20000 objects. It should be noted that although the algorithm CARRDG is proposed for the special problem, in fact it solves, however, the problem of the transformation and simplification of logic expressions from conjunctive normal form to disjunctive normal form, so it has wide variety of application fields, and it maight also open a new window for solving the combination exposition problems in the real-world problems.
In the sixth chapter, the tentative idea for the future structure of the SDTCM system with learning mechanism is proposed, and the application means and the significance of our research fruits in the structure are interpreted. At last, the main research work and the existing problems of this dissertation are concluded and the future of the research is also prospected.
引文
[1]U.Fayyad,G.Piatetsky-Shapiro,P.Smyth.The KDD process for extracting useful knowledge from volumes of data[J].Communications of the ACM,1996,39(11):27-34.
[2]U.Fayyad,G.Piatetsky-Shapiro,P.Smyth.From data mining to knowledge discovery:an overview[J].Advances in Knowledge Discovery and Data Mining,1996:1-34.
[3]U.Fayyad,G.Piatetsky-Shapiro,P.Smyth.Data mining and knowledge discovery:towards a unifying framework[A].Proceedings of the Second International Conference on Knowledge Discovery and Data Mining(KDD96)[C],AAAI Press,1996.
[4]http://www.digital.com/info/onlinelab/gen/gen.html.
[5]http://www.hncs.com.
[6]http://www.smr.nl.
[7]http://www.kdnuggets.com/solution/hr-profiler.html.
[8]http://www.research.ibm.com/scout.
[9]杨立.基于领域知识的知识发现研究[D].中国科学院博士学位论文,2005.
[10]人民日报社论.坚定不移地贯彻执行党的中医政策[N].人民日报,1980-03-27.
[11]秦笃烈,鲍亦万.中医计算机模拟及专家系统概论[M].北京:人民卫生出版社,1989.
[12]朱文锋.中医(辅助)诊疗系统的研究[J].中国中医基础医学杂志,2003,9(10):8-11.
[13]http://www.shanghai-ison.com.cn/zymzdyb.htm
[14]http://www.lian-shi.com/alone.htm
[15]http://www.hnctcm.com/zyzdx/cgzs.htm
[16]http://www.cmauk.org/
[17]http://www.medinformatics.org/mdi207/
[18]林维鉴.中医专家系统研究的反思及其对策[J].福建中医学院学报,1997.7(1):628.
[19]周昌乐,张志枫.智能中医诊断信息处理技术研究进展与展望[J].中西医结合学报,2006,4(6):560-566.
[20]胡镜清,赖世隆.中医诊断现代化研究的基本内容和关键环节[J].中国中医基础医学杂志,2000,6(6):3-5.
[21]L.A.Zadeh.What is soft computing[J].Soft Computing,1997,1(1):Pref-ace.
[22]王新华.中医基础理论[M].北京:人民卫生出版社,2001.
[23]柯雪帆.中医辨证学[M].上海:上海中医学院出版社,1987
[24]周慧生.中医模糊诊断方法[J].中国中医基础医学杂志,1999,5(10):8-9.
[25]吕汉兴,孙德保,程良铨,等.中医专家系统辨证推理的决策模型[J].华中理工大学学报,1989,17(6):67-71.
[26]叶建红.2型糖尿病中医疗效的模糊综合评判[J].现代康复,2001,5(10):106.
[27]侯风刚,赵刚.中医证候量化诊断标准研究存在问题的思考[J].中医药学刊,2004,22(9):1622-1623.
[28]张定一.中医辨证的几个模糊数学模型[J].数理医药学杂志,1994,7(1):10-11.
[29]丁占鳌.用计算机模拟中医辨证诊断思维的一般程序[J].黑龙江财专学报,1988,(2):34-39.
[30]刘龙,许玲,李渡华,等.应用模糊数学研究中医药的现状[J].中医临床医学,2004,11(6):934-936.
[31]林剑鸣.中医现代化与数学[J].数理医药学杂志,2003,16(3):256-257.
[32]魏威,魏欣甫.模糊数学与中辨证论治[J].中医教育,1999,18(4):54-56.
[33]阎伟,李明伟,梁华孟.模糊模式识别在儿科咳嗽辨证分型中的应用[J].四川中医1999,17(5):20.
[34]于铁成.中医基本理论研究要跟上时代的进步[J].天津中医学院学报,2003,22(1):1-4.
[35]刘亚娴.模糊数学在中医临床的应用[J].陕西中医学院学报,2000,23(6):5-6。
[36]童家乐,吴敏.模糊数学在中医研究生论文评价中的运用[J].中医教育,1998,17(1):26-27.
[37]李波,钟智.肝炎病诊断的模糊数学模型[J].广西医科大学学报,1998,15(1):52-54.
[38]龙新生,熊曼琪.论数学方法在《伤寒论》研究中的应用[J].中医药信息,1998,(3):10.
[39]孙益鑫.论模糊数学与中医学[J].中国医药学报,1996,11(1):15-19.
[40]王健生.模糊数学在中医诊断中的应用[J].甘肃中医学院学报,1992,9(2):52,45.
[41]黄煜宇,张继增.中医应用模糊数学的探讨[J].中国中医药信息杂志,2004,11(10):933-934.
[42]孙益鑫,夏善玲.中医证本质研究的方法学探析[J].医学与哲学,1997,18(4):184-186.
[43]郭戊英.中医与模糊数学[J].上海第二工业大学学报,1987,(2):64-75.
[44]杨松涛.中药组方中的模糊分析法[J].安徽中医学院学报,1997,16(5):48-49.
[45]刘兰林,张辉.论温病学的研究借助模糊数学的可行性[J].中医教育,2001,20(6):7-8.
[46]陈荣山,陈东汉.模糊数学在中医脉象模式识别中的应用[J].医学信息,1998,11(3):20-21.
[47]周鲁,唐向阳,付超等.解表类中药的模糊聚类分析[J].华西药学杂志,2004,19(5):339-341.
[48]施明辉,周昌乐.人工神经网络在中医诊断中的应用研究现状与趋势[J].中国中医药信息杂志,2007,14(1):2-5.
[49]陈五零,王存冉,郭荣江.神经元网络模型及其在中医诊断方面的应用[J].中华医学杂志,1991,71(2):111-113.
[50]陈伟清.浅论人工神经网络在中医学上的应用[J].河南中医学院学报.2004,19(4):12-13.
[51]吴新根,吕维雪.一个用于肝病诊断的连接主义专家[J].中国生物医学工程学报,1996,15(1):62-66.
[52]张海男,胡随瑜,陈泽奇,等.抑郁症常见中医证候类型第一轮专家问卷分析[J].湖南医科大学学报.2002,27(6):519-521.
[53]樊晓平,彭展,杨胜跃,等.基于多层前馈型人工神经网络的抑郁症分类系统研究[J],计算机工程与应用,2004,13:205-208.
[54]边沁,何裕民,施小成,等.基于MFBP算法的中医证型的神经网络模型初探[J].中国中医基础医学杂志,2001,7(5):66-69.
[55]叶进,邢传鼎.基于人工神经网络的病症诊断原型系统[J].东华大学学报(自然科学版),2003,29(4):43-47.
[56]林维鉴.BP网络用于中医痹证证候分类[J].福建中医学院学报,1997,7(4):41-43.
[57]周志坚,王宗源,邓兆智.神经网络在类风湿性关节炎病情分级中的应用初探[J].生物医学工程学杂志,1999,16(4):479-482.
[58]施明辉,周昌乐,吴清锋,等.用神经网络实现基于舌诊的八纲辨证推理初探[A].中国人工智能进展:2005(下册)[C].北京:北京邮电大学出版社,2005.
[59]白云静,申洪波,孟庆刚,等.中医证候研究的人工神经网络方法探析[J].中医药学刊,2004,22(12):2221-2223.
[60]王继成,吕维雪.一个基于符号神经网络的心电图分类系统[J].中国生物医学工程学报,1996,15(3):202-207.
[61]陈玲,李为民,一个基于NN的高血压中医诊疗专家系统原型[J].计算机工程,1994,20(2):23-26.
[62]赵卫东,盛昭瀚,杜雪寒.基于神经网络的案例推理医疗诊断[J].东南大学学报(自然科学版),2000,130(13):46-50.
[63]秦中广,毛宗源.粗糙神经网络及其在中医智能诊断系统中的应用[J].计算机工程与应用,2001,18:34-35.
[64]徐方维,蔡坤宝.人工神经网络在中医脉象信号检测中的应用[J].重庆大学学报,2004,27(8):35-38.
[65]王炳和,相敬林.基于神经网络方法的人体脉象识别研究[J].西北工业大学学报,2002,20(3):454-457.
[66]王璐,吴南健,温殿忠.人工神经网络在孕妇脉象判别中的应用[J].黑龙江大学自然科学学报,2003,20(4):65-68,72.
[67]赵忠旭,沈兰荪,卫保国等.基于人工神经网络的彩色校正方法研究[J].中国图象图形学报,2000,5(A)(9):785-789.
[68]秦中广,毛宗源,邓兆智.基于Rough Set的中医类风湿诊断知识抽取[J].华南理工大学学报,2000,28(4):30-34.
[69]秦中广,毛宗源,邓兆智.粗糙集在中医类风湿证候诊断中的应用[J].中国生物医学工程学报,2002,20(4):358-363.
[70]何建敏,于跃海.基于粗集理论的医学诊断规则提取方法[J].系统工程学报,2002,17(6):519-525.
[71]何先华,赵卫东,盛昭瀚.粗集在医疗诊断知识支持中的应用[J].计算机工程与应用,2001,(20):166-168.
[72]王相东,殷鑫.粗糙集理论与证候规范化研究[J].陕西中医学院学报,2005,28(2):70-71.
[73]邰东梅,赵明芳.试论中医诊断的数理智能化[J].中国中医基础医学杂志,2005,11(10):780-781.
[74]谢国明.基于粗集理论的中医诊断模型的建立[J].数理医药学杂志,2005,18(4):302-304.
[75]秦中广,毛宗源.粗糙神经网络及其在中医智能诊断系统中的应用[J].计算机工程与应用,2001,(18):34-35.
[76]L.A.Zadeh.Fuzzy sets[J].Information and Control.1965,8(3):338-353.
[77]Z.Pawlak.Rough sets[J].International Journal of Information and Computer Science.1982,(11):341-356.
[78]L.A.Zadeh.Fuzzy set theory-a perspective[A].Fuzzy Automata and Decision Processes[M].M.Gupta(edts).North-Holland,1977:3-4.
[79]Z.Pawlak.Rough sets and intelligent data analysis[J].Information Sciences.2002,147:1-12.
[80]Z.Pawlak.Rough sets:theoretical aspects of reasoning about data[M].Kluwer Academic Publishers,Boston,London,Dordrecht,1991.
[81]http://logic.mimuw.edu.pl/rses/
[82]http://rosetta.lcb.uu.se/general/
[83]http://rsds.wsiz.rqeszow.pl/
[84]M.Banerjee,M.K.Chakraborty.Algeras from rough sets[A].In:S.K.Pal,L.Polkowski,A.Skowron(eds),Rough-Neural Computing:Techniques for Computing with Words,Cognitive Technologies.Springer Verlag,Heidelberg,2004:157-188.
[85]A.Skowron.Rough sets and vague concepts[J].Fundamenta Informaticae,2005,64:417-431.
[86]E.A.Rady,A.M.Kozae,M.M.E.Abd El-Mouser.Generalized rough sets[J].Chaos,Solutions and Fractals,2004,21:49-53.
[87]A.M.Radzikowska,E.E.Zerre.A comparative study of fuzzy rough sets[J].Fuzzy Sets and Systems,2002,126:137-155
[88]K.Qin,Z.Pei.On the topological properties of fuzzy rough sets[J].Fuzzy Sets and Systems,2005,151:601-613.
[89]张文修,姚一豫,梁怡.粗糙集与概念格[M],西安:西安交通大学出版社,2006.
[90]A.G.Jackson,Z.Pawlak,S.R.LeClair.Rough sets applied to the discovery of materials knowledge[J].Journal of Alloys and Compounds,1998,279:14-21.
[91]S.Tsumoto.Automated induction of medical expert system rules from clinical databases based on rough set theory[J].Information Sciences,1998,(112):67-84.
[92]S.Tsumoto.Mining diagnostic rules from clinical databases using rough sets and medical diagnoatic model[J].Information Sciences,2004,(162):65-80.
[93]施明辉,周昌乐.一种基于粗糙集理论的分类规则提取算法[J].计算机工程与应用,2006,42(9):150-153.
[94]M.H.Shi,C.L.Zhou.Approach to knowledge discovery for capturing experts'reasoning based on rough set theory[J].Journal of Computational Information Systems,2005,1:887-894.
[95]A.Skowron,C.Rauszer,R.Slowinski.Intelligent decision support handbook of applications and advances of the rough sets theory[M].Dordrecht:Kluwer Academic Publishers,1992.331-362.
[96]B.Walczak,D.L Massart.Rough sets theory[J].Chemometrics and Intelligent Laboratory Systems 1999,47:1-16.
[97]J.Wang,J.Wang.Reduction algorithms based on discernibility matrix:the ordered attributes method[J].Journal of Computer Science & Technology,2001,16(6):489-504.
[98]刘银山,吴孟达,王丹.粗糙集中求取所有最小属性约简快速算法[J].计算机工程与科学,2007,29(1):97-101.
[99]常犁云,王国胤,吴渝.一种基于Rough Set理论的属性约简及规则提取方法[J].软件学报,1999,10(11):1206-1211.
[100]刘洋,冯博琴,周江卫.基于差别矩阵的增量式属性约简完备算法[J].西安交通大学学报,2007,41(2):158-161,208.
[101]叶东毅.Jelonek属性约简算法的一个改进[J].电子学报,2000,(12).
[102]王珏,王任,苗夺谦,等.基于Rough Set理论的“数据浓缩”[J].计算机学报,1998,21(05):393-399.
[103]R.Felix,T.Ushio.Rough sets-based machine learning using a binary discernibility matrix[M].IPMM'99 published,1999,299-305.
[104]支天云,苗夺谦.二进制可辨矩阵的变换及高效属性约简算法的构造[J].计算机科学,2002,29(2):140-142,封四.
[105]徐章艳,杨炳儒,宋威.基于简化的二进制差别矩阵的快速属性约简算法[J].计算机科学,2006,33(4):155-158.
[106]黄龙军,章志明,周才英,等.一种基于布尔矩阵的属性约简方法[J].计算机工程与应用,2006,42(9):160-161,181.
[107]李龙星,运士伟,杨炳儒.基于布尔矩阵表示的粗集属性约简启发式算法[J].计算机工程,2007,33(10):205-206.
[108]J.Jelonek,K.Krawiec,R.Slowinski.Rough set reduction of attributes and their domains for neural networks[J].International Journal of Computational Intelligence,1995,11(2):339-347.
[109]X.H.Hu,N.Cercone.Learning in relational databases:a rough set approach [J].Computational Intelligence,1995,11(2):323-338.
[110]J.W.Guan,D.A.Bell.Rough computational methods for information systems [J].Artificial Intelligences,1998,105(1-2):77-103.
[111]杨明,倪魏伟,孙志挥.一种新颖的最小属性约简模型[J].东南大学学报(自然科学版),2004,34(05):604-608
[112]刘少辉,盛秋戬,吴斌,史忠植,胡斐.Rough集高效算法的研究[J].计算机学报,2003,26(05):524-529.
[113]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):759-766.
[114]苗夺谦,胡桂荣.知识约简的一种启发式算法[J].计算机研究与发展,1999,36(06):681-684.
[115]J.Woblewski.Finding minimal reducts using genetic algorithms[A].In:Wang PP,ed.Proceedings of the International Workshop on Rough Sets Soft Computing at Second Annual Joint Conference on Information Sciences (JCIS 95)[C].Wrightsville Beach,North Carolina,USA,1995,186-189.
[116]朱江华,李海波,潘丰.基于遗传算法和模糊粗糙集的知识约简[J].计算机仿真,2007,24(1):89,119.
[117]M.Kryszkiewicz,H.Rybinski.Computation of reducts of composed information systems[J],Fundamenta Informaticae,1996,27(2-3):183-195.
[118]T.Y.Lin,N.Cercone(Eds.).Rough sets and data mining:analysis of imperfect data[M],Kluwer Academic Publishers,Boston,USA,1997.
[119]L.Polkowski,A.Skowron(Eds.).Rough sets in knowledge discovery 2:applications,case studies and software systems,studies in fuzziness and soft computing vol.19[M],Physica-Verlag,Heidelberg,1998.
[120]J.Wroblewski.Analyzing relational databases using rough set based methods [A],in:Eighth International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems IPMU.Madrid,Spain,vol.I[C],2000:256-262.
[121]T.P.Hong,L.H.Tseng,S.L.Wang.Learning rules from incomplete training examples by rough sets[J].Expert Systems with Applications,2002,22:285-293.
[122]Z.Pawlak.An inquiry into anatomy of conflicts[J].Journal of Information Sciences,1998,109:65-78.
[123]Z.Pawlak.Some remarks on conflict analysis[J].European Journal of Operational Research,2005,166:649-654.
[124]W.Z.Wu,W.X.Zhang,H.Z.Li.Knowledge acquisition in incomplete fuzzy information systems via the rough set approacch[J].Experts Systems,2003,20(5):280-286.
[125]Z.Pawlak.Rough set approach to knowledge-based decision support[J].European Journal of Operational Research,1997,99:48-57.
[126]W.Ziarko,J.Katzberg.Control algorithms acquisition,analysis and reduction:machine learning approach[A].in:Knowledge-Based Systems Diagnosis,Supervision and Control.Plenum Press,Oxford,1989:167-178.
[127] R. Slowinski, C. Zopounidis. Rough set sorting of firms according to bankruptcy risk [A], in: M. Paruccini (ed.). Applying Multiple Criteria Aid for Decision to Environmental Management. Kluwer, Dordrecht. Nether- lands, 1994, 339-357.
[128] D. Brindle. Speaker-independent speech recognition by rough sets analysis[A]. in: T. Y. Lin. (ed.). The Third International Workshop on Rough Sets and Soft Computing Proceedings (RSSC'94), San Jose State University, San Jose, California, USA, 1995: 85-88.
[129] S.K.M. Wong, W. Ziarko. On optimal decision rules in decision tables[D]. Bulletin of Polish Academy of Sciences, 1985, 33(11/12): 693-696.
[130] A. Skowron and C. Rauszer, The discernibility matrices and functions in information systems [A]. In: R. Slowinski, Editor, Intelligent Decision Support - Handbook of Applications and Advances of the Rough Sets Theory, System Theory, Knowledge Engineering and Problem Solving vol. 11[M], Kluwer Academic Publishers, Dordrecht, The Netherlands, 1992: 331-362.
[131] H. Tanaka et al. Fuzzy inference system based on rough sets and its application to Medical diagnosis [A]. Intelligent Decision Support-Handbook of Applications and Advances of Rough Sets Theory [M], Kluwer Academic PPublishers, Dordrecht, Boston, London, 1992: 111-118.
[132] G.I. Paterson et al. Rough classification of pneumonia patients using a clinical databases[A]. Rough Sets, Fuzzy Sets and Knowledge Discovery[M], Springerverge, 1994: 412-419.
[133] L.K. Woolery et al. Machine learning for an expert system to predict pretern birth risk[J]. Journal of the American Informatics Association, 1994, 1(6): 439-446.
[134] Z. Pawlak. Rough classification of patients after highly selective vagotomy for duodenal ulcer[J]. International Journal Man-Machine Studies, 1986, 24: 413-433.
[135]徐佩绅,华蕴博,陆金芳,等.中医经验的形式化和计算机辨证模型—泮澄濂肝病经验的程序设计[J].中国生物医学工程学报,1985,4(1):1-6.
[136]朱文锋.现代中医临床诊断学[M].北京:人民卫生出版社,2003.
[137]阮达,黄崇福.模糊集与模糊信息粒理论[M])北京:北京师范大学出版社,2000.
[138]朱咏华,朱文锋.中医症状的规范化研究[J].湖南中医学院学报,2002,22(3):35-37.
[139]朱文锋.证、症、征等词的概念与演变[J].科学术语研究,2003,5(4):20-21.
[140]朱文锋,黄碧群.证、证候的辨析与规范[J].山西中医,2005,21(3):1-3.
[141]胡金亮,李建生,余学庆.中医证候诊断标准研究背景与现状[J].河南中医学院学报,2005,20(3):77-79.
[142]施明辉,周昌乐.一种基于ANN的中医辨证不确定性推理模型研究[J].计算机工程与应用,2007,43(7):10-13,27.
[143]施明辉,周昌乐.一种用ANN实现带权不确定性推理的方法[J].哈尔滨工业大学学报,2007,39(9):1491-1495.
[144]王继成,吕维雪.一个基于符号神经网络的心电图分类系统[J].中国生物医学工程学报,1996,15(3):202-207.
[145]T.M.Mitchell.Machine learning[M].Mc Graw-Hill,Portland,1997.
[146]D.Michie,D.Spiegelhalter,C.C.Taloy.Machine learning,neural and statistical classification[M].Eills Horwood Limited,England,1994.
[147]J.R.Quinlan.C4.5-Programs for Machine Learning[M].Morgan Kaufmann,Palo Alto,1993.
[148]韩祯祥,张琦,文福拴.粗糙集理论及其应用综述[J].控制理论与应用,1999,16(2):153-165.
[149] P.S. Michalski, I. Mozetic, J. Hong, et. al. The multi-purpose incremental learning system AQ15 and its testing application to three medical domains [A], in: Proceedings of the Fifth National Conference on Artificial Intelli- gence[C], AAAI Press,Mento Park, 1986, 1041-1045.
[150] A. Skowron, J. Grzymala-Busse. From rough set theory to evidence theory [A]. in:R. Yager, M. Fedrizzi, J. Kacprzyk Eds. Advances in the Dempster-shafer Theory of Evidence[M], John Wiley&Sons, NY, 1994, 193-236.
[151] B. Walczak, D.L. Massart. Rough sets theory [J]. Chemometrics and Intelligent Laboratory Systems, 1999, (47): 1-16.
[152] M.H. Shi, C.L. Zhou. Diagnostic rules discovery with hierarchical clustering and focusing mechanism based on rough sets theory[A], Proceedings of the Fourth International Conference on Fuzzy System and Knowledge Discovery (FSKD 2007) [C]: 673-677.
[153] S. Tsumoto. Rule induction with grouping target concepts based on rough sets[R]. Electronic Notes in Theoretical Computer Science, 2003, 82(4): 1- 12. http://www.elsevier.nl/locate/entcs/volume82.html.
[154] ftp://ftp.ics.uci.edu/pub/machine—learning-databases/
[155] G.Y. Wang. Rough reduction in algebra view and information view[J], International Journal of Intelligent Systems, 2003, (18): 679-688.
[156] Z. Pawlak, A. Skowron. Rough sets and Boolean reasoning[J], Information Sciences, 2007, 177: 41-73.
[157] J.W. Grzymala-Busse. Data with missing attribute values: generalization of indiscernibility relation and rule reduction [A]. Transactions on Rough Sets I[M], LNCS 3100, Springer-Verlag, Berlin, 2004.
[158] M. Kryszkiewicz. Rough set approach to incomplete information systems [J]. Information Sciences, 112: 33-49.
[159]M.Kryszkiewicz.Rules in incomplete information systems[J].Information Sciences,1999,113:271-292.
[160]Y.Leung,W.Z.Wu,W.X.Zhang.Knowledge acquisition in incomplete information systems:a rough set approach[J],European Journal of Operational Research,2006,168:164-180.
[161]R.Latkowski.Flexible indiscernibility relations for missing attribute values [J].Fundamenta Informaticae,2005,67:131-147.
[162]X.Hu,N.Cercone.Data mining via discretization,generalization and rough set feature selection[J].Knowledge and Information Systems:An International Journal,1999,1(1):33-60.
[163]H.S.Nguyen.Discretization of real value attributes,boolean reasoning approach [D].Warsaw University,Warsaw,Poland,1997.
[164]H.S.Nguyen.From optimal hyperplanes to optimal decision trees[J].Fundamenta Informaticae,1998,34(1-2):145-174.
[165]J.W.Grzymala-Busse,W.Ziarko.Data mining and rough set theory[J].Communications of the ACM,2000,43:108-109.
[166]H.S.Nguyen.On efficient handling of continuous attributes in large data bases[J].Fundamenta Informaticae,2001,48(1):61-81.
[2]U.Fayyad,G.Piatetsky-Shapiro,P.Smyth.From data mining to knowledge discovery:an overview[J].Advances in Knowledge Discovery and Data Mining,1996:1-34.
[3]U.Fayyad,G.Piatetsky-Shapiro,P.Smyth.Data mining and knowledge discovery:towards a unifying framework[A].Proceedings of the Second International Conference on Knowledge Discovery and Data Mining(KDD96)[C],AAAI Press,1996.
[4]http://www.digital.com/info/onlinelab/gen/gen.html.
[5]http://www.hncs.com.
[6]http://www.smr.nl.
[7]http://www.kdnuggets.com/solution/hr-profiler.html.
[8]http://www.research.ibm.com/scout.
[9]杨立.基于领域知识的知识发现研究[D].中国科学院博士学位论文,2005.
[10]人民日报社论.坚定不移地贯彻执行党的中医政策[N].人民日报,1980-03-27.
[11]秦笃烈,鲍亦万.中医计算机模拟及专家系统概论[M].北京:人民卫生出版社,1989.
[12]朱文锋.中医(辅助)诊疗系统的研究[J].中国中医基础医学杂志,2003,9(10):8-11.
[13]http://www.shanghai-ison.com.cn/zymzdyb.htm
[14]http://www.lian-shi.com/alone.htm
[15]http://www.hnctcm.com/zyzdx/cgzs.htm
[16]http://www.cmauk.org/
[17]http://www.medinformatics.org/mdi207/
[18]林维鉴.中医专家系统研究的反思及其对策[J].福建中医学院学报,1997.7(1):628.
[19]周昌乐,张志枫.智能中医诊断信息处理技术研究进展与展望[J].中西医结合学报,2006,4(6):560-566.
[20]胡镜清,赖世隆.中医诊断现代化研究的基本内容和关键环节[J].中国中医基础医学杂志,2000,6(6):3-5.
[21]L.A.Zadeh.What is soft computing[J].Soft Computing,1997,1(1):Pref-ace.
[22]王新华.中医基础理论[M].北京:人民卫生出版社,2001.
[23]柯雪帆.中医辨证学[M].上海:上海中医学院出版社,1987
[24]周慧生.中医模糊诊断方法[J].中国中医基础医学杂志,1999,5(10):8-9.
[25]吕汉兴,孙德保,程良铨,等.中医专家系统辨证推理的决策模型[J].华中理工大学学报,1989,17(6):67-71.
[26]叶建红.2型糖尿病中医疗效的模糊综合评判[J].现代康复,2001,5(10):106.
[27]侯风刚,赵刚.中医证候量化诊断标准研究存在问题的思考[J].中医药学刊,2004,22(9):1622-1623.
[28]张定一.中医辨证的几个模糊数学模型[J].数理医药学杂志,1994,7(1):10-11.
[29]丁占鳌.用计算机模拟中医辨证诊断思维的一般程序[J].黑龙江财专学报,1988,(2):34-39.
[30]刘龙,许玲,李渡华,等.应用模糊数学研究中医药的现状[J].中医临床医学,2004,11(6):934-936.
[31]林剑鸣.中医现代化与数学[J].数理医药学杂志,2003,16(3):256-257.
[32]魏威,魏欣甫.模糊数学与中辨证论治[J].中医教育,1999,18(4):54-56.
[33]阎伟,李明伟,梁华孟.模糊模式识别在儿科咳嗽辨证分型中的应用[J].四川中医1999,17(5):20.
[34]于铁成.中医基本理论研究要跟上时代的进步[J].天津中医学院学报,2003,22(1):1-4.
[35]刘亚娴.模糊数学在中医临床的应用[J].陕西中医学院学报,2000,23(6):5-6。
[36]童家乐,吴敏.模糊数学在中医研究生论文评价中的运用[J].中医教育,1998,17(1):26-27.
[37]李波,钟智.肝炎病诊断的模糊数学模型[J].广西医科大学学报,1998,15(1):52-54.
[38]龙新生,熊曼琪.论数学方法在《伤寒论》研究中的应用[J].中医药信息,1998,(3):10.
[39]孙益鑫.论模糊数学与中医学[J].中国医药学报,1996,11(1):15-19.
[40]王健生.模糊数学在中医诊断中的应用[J].甘肃中医学院学报,1992,9(2):52,45.
[41]黄煜宇,张继增.中医应用模糊数学的探讨[J].中国中医药信息杂志,2004,11(10):933-934.
[42]孙益鑫,夏善玲.中医证本质研究的方法学探析[J].医学与哲学,1997,18(4):184-186.
[43]郭戊英.中医与模糊数学[J].上海第二工业大学学报,1987,(2):64-75.
[44]杨松涛.中药组方中的模糊分析法[J].安徽中医学院学报,1997,16(5):48-49.
[45]刘兰林,张辉.论温病学的研究借助模糊数学的可行性[J].中医教育,2001,20(6):7-8.
[46]陈荣山,陈东汉.模糊数学在中医脉象模式识别中的应用[J].医学信息,1998,11(3):20-21.
[47]周鲁,唐向阳,付超等.解表类中药的模糊聚类分析[J].华西药学杂志,2004,19(5):339-341.
[48]施明辉,周昌乐.人工神经网络在中医诊断中的应用研究现状与趋势[J].中国中医药信息杂志,2007,14(1):2-5.
[49]陈五零,王存冉,郭荣江.神经元网络模型及其在中医诊断方面的应用[J].中华医学杂志,1991,71(2):111-113.
[50]陈伟清.浅论人工神经网络在中医学上的应用[J].河南中医学院学报.2004,19(4):12-13.
[51]吴新根,吕维雪.一个用于肝病诊断的连接主义专家[J].中国生物医学工程学报,1996,15(1):62-66.
[52]张海男,胡随瑜,陈泽奇,等.抑郁症常见中医证候类型第一轮专家问卷分析[J].湖南医科大学学报.2002,27(6):519-521.
[53]樊晓平,彭展,杨胜跃,等.基于多层前馈型人工神经网络的抑郁症分类系统研究[J],计算机工程与应用,2004,13:205-208.
[54]边沁,何裕民,施小成,等.基于MFBP算法的中医证型的神经网络模型初探[J].中国中医基础医学杂志,2001,7(5):66-69.
[55]叶进,邢传鼎.基于人工神经网络的病症诊断原型系统[J].东华大学学报(自然科学版),2003,29(4):43-47.
[56]林维鉴.BP网络用于中医痹证证候分类[J].福建中医学院学报,1997,7(4):41-43.
[57]周志坚,王宗源,邓兆智.神经网络在类风湿性关节炎病情分级中的应用初探[J].生物医学工程学杂志,1999,16(4):479-482.
[58]施明辉,周昌乐,吴清锋,等.用神经网络实现基于舌诊的八纲辨证推理初探[A].中国人工智能进展:2005(下册)[C].北京:北京邮电大学出版社,2005.
[59]白云静,申洪波,孟庆刚,等.中医证候研究的人工神经网络方法探析[J].中医药学刊,2004,22(12):2221-2223.
[60]王继成,吕维雪.一个基于符号神经网络的心电图分类系统[J].中国生物医学工程学报,1996,15(3):202-207.
[61]陈玲,李为民,一个基于NN的高血压中医诊疗专家系统原型[J].计算机工程,1994,20(2):23-26.
[62]赵卫东,盛昭瀚,杜雪寒.基于神经网络的案例推理医疗诊断[J].东南大学学报(自然科学版),2000,130(13):46-50.
[63]秦中广,毛宗源.粗糙神经网络及其在中医智能诊断系统中的应用[J].计算机工程与应用,2001,18:34-35.
[64]徐方维,蔡坤宝.人工神经网络在中医脉象信号检测中的应用[J].重庆大学学报,2004,27(8):35-38.
[65]王炳和,相敬林.基于神经网络方法的人体脉象识别研究[J].西北工业大学学报,2002,20(3):454-457.
[66]王璐,吴南健,温殿忠.人工神经网络在孕妇脉象判别中的应用[J].黑龙江大学自然科学学报,2003,20(4):65-68,72.
[67]赵忠旭,沈兰荪,卫保国等.基于人工神经网络的彩色校正方法研究[J].中国图象图形学报,2000,5(A)(9):785-789.
[68]秦中广,毛宗源,邓兆智.基于Rough Set的中医类风湿诊断知识抽取[J].华南理工大学学报,2000,28(4):30-34.
[69]秦中广,毛宗源,邓兆智.粗糙集在中医类风湿证候诊断中的应用[J].中国生物医学工程学报,2002,20(4):358-363.
[70]何建敏,于跃海.基于粗集理论的医学诊断规则提取方法[J].系统工程学报,2002,17(6):519-525.
[71]何先华,赵卫东,盛昭瀚.粗集在医疗诊断知识支持中的应用[J].计算机工程与应用,2001,(20):166-168.
[72]王相东,殷鑫.粗糙集理论与证候规范化研究[J].陕西中医学院学报,2005,28(2):70-71.
[73]邰东梅,赵明芳.试论中医诊断的数理智能化[J].中国中医基础医学杂志,2005,11(10):780-781.
[74]谢国明.基于粗集理论的中医诊断模型的建立[J].数理医药学杂志,2005,18(4):302-304.
[75]秦中广,毛宗源.粗糙神经网络及其在中医智能诊断系统中的应用[J].计算机工程与应用,2001,(18):34-35.
[76]L.A.Zadeh.Fuzzy sets[J].Information and Control.1965,8(3):338-353.
[77]Z.Pawlak.Rough sets[J].International Journal of Information and Computer Science.1982,(11):341-356.
[78]L.A.Zadeh.Fuzzy set theory-a perspective[A].Fuzzy Automata and Decision Processes[M].M.Gupta(edts).North-Holland,1977:3-4.
[79]Z.Pawlak.Rough sets and intelligent data analysis[J].Information Sciences.2002,147:1-12.
[80]Z.Pawlak.Rough sets:theoretical aspects of reasoning about data[M].Kluwer Academic Publishers,Boston,London,Dordrecht,1991.
[81]http://logic.mimuw.edu.pl/rses/
[82]http://rosetta.lcb.uu.se/general/
[83]http://rsds.wsiz.rqeszow.pl/
[84]M.Banerjee,M.K.Chakraborty.Algeras from rough sets[A].In:S.K.Pal,L.Polkowski,A.Skowron(eds),Rough-Neural Computing:Techniques for Computing with Words,Cognitive Technologies.Springer Verlag,Heidelberg,2004:157-188.
[85]A.Skowron.Rough sets and vague concepts[J].Fundamenta Informaticae,2005,64:417-431.
[86]E.A.Rady,A.M.Kozae,M.M.E.Abd El-Mouser.Generalized rough sets[J].Chaos,Solutions and Fractals,2004,21:49-53.
[87]A.M.Radzikowska,E.E.Zerre.A comparative study of fuzzy rough sets[J].Fuzzy Sets and Systems,2002,126:137-155
[88]K.Qin,Z.Pei.On the topological properties of fuzzy rough sets[J].Fuzzy Sets and Systems,2005,151:601-613.
[89]张文修,姚一豫,梁怡.粗糙集与概念格[M],西安:西安交通大学出版社,2006.
[90]A.G.Jackson,Z.Pawlak,S.R.LeClair.Rough sets applied to the discovery of materials knowledge[J].Journal of Alloys and Compounds,1998,279:14-21.
[91]S.Tsumoto.Automated induction of medical expert system rules from clinical databases based on rough set theory[J].Information Sciences,1998,(112):67-84.
[92]S.Tsumoto.Mining diagnostic rules from clinical databases using rough sets and medical diagnoatic model[J].Information Sciences,2004,(162):65-80.
[93]施明辉,周昌乐.一种基于粗糙集理论的分类规则提取算法[J].计算机工程与应用,2006,42(9):150-153.
[94]M.H.Shi,C.L.Zhou.Approach to knowledge discovery for capturing experts'reasoning based on rough set theory[J].Journal of Computational Information Systems,2005,1:887-894.
[95]A.Skowron,C.Rauszer,R.Slowinski.Intelligent decision support handbook of applications and advances of the rough sets theory[M].Dordrecht:Kluwer Academic Publishers,1992.331-362.
[96]B.Walczak,D.L Massart.Rough sets theory[J].Chemometrics and Intelligent Laboratory Systems 1999,47:1-16.
[97]J.Wang,J.Wang.Reduction algorithms based on discernibility matrix:the ordered attributes method[J].Journal of Computer Science & Technology,2001,16(6):489-504.
[98]刘银山,吴孟达,王丹.粗糙集中求取所有最小属性约简快速算法[J].计算机工程与科学,2007,29(1):97-101.
[99]常犁云,王国胤,吴渝.一种基于Rough Set理论的属性约简及规则提取方法[J].软件学报,1999,10(11):1206-1211.
[100]刘洋,冯博琴,周江卫.基于差别矩阵的增量式属性约简完备算法[J].西安交通大学学报,2007,41(2):158-161,208.
[101]叶东毅.Jelonek属性约简算法的一个改进[J].电子学报,2000,(12).
[102]王珏,王任,苗夺谦,等.基于Rough Set理论的“数据浓缩”[J].计算机学报,1998,21(05):393-399.
[103]R.Felix,T.Ushio.Rough sets-based machine learning using a binary discernibility matrix[M].IPMM'99 published,1999,299-305.
[104]支天云,苗夺谦.二进制可辨矩阵的变换及高效属性约简算法的构造[J].计算机科学,2002,29(2):140-142,封四.
[105]徐章艳,杨炳儒,宋威.基于简化的二进制差别矩阵的快速属性约简算法[J].计算机科学,2006,33(4):155-158.
[106]黄龙军,章志明,周才英,等.一种基于布尔矩阵的属性约简方法[J].计算机工程与应用,2006,42(9):160-161,181.
[107]李龙星,运士伟,杨炳儒.基于布尔矩阵表示的粗集属性约简启发式算法[J].计算机工程,2007,33(10):205-206.
[108]J.Jelonek,K.Krawiec,R.Slowinski.Rough set reduction of attributes and their domains for neural networks[J].International Journal of Computational Intelligence,1995,11(2):339-347.
[109]X.H.Hu,N.Cercone.Learning in relational databases:a rough set approach [J].Computational Intelligence,1995,11(2):323-338.
[110]J.W.Guan,D.A.Bell.Rough computational methods for information systems [J].Artificial Intelligences,1998,105(1-2):77-103.
[111]杨明,倪魏伟,孙志挥.一种新颖的最小属性约简模型[J].东南大学学报(自然科学版),2004,34(05):604-608
[112]刘少辉,盛秋戬,吴斌,史忠植,胡斐.Rough集高效算法的研究[J].计算机学报,2003,26(05):524-529.
[113]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):759-766.
[114]苗夺谦,胡桂荣.知识约简的一种启发式算法[J].计算机研究与发展,1999,36(06):681-684.
[115]J.Woblewski.Finding minimal reducts using genetic algorithms[A].In:Wang PP,ed.Proceedings of the International Workshop on Rough Sets Soft Computing at Second Annual Joint Conference on Information Sciences (JCIS 95)[C].Wrightsville Beach,North Carolina,USA,1995,186-189.
[116]朱江华,李海波,潘丰.基于遗传算法和模糊粗糙集的知识约简[J].计算机仿真,2007,24(1):89,119.
[117]M.Kryszkiewicz,H.Rybinski.Computation of reducts of composed information systems[J],Fundamenta Informaticae,1996,27(2-3):183-195.
[118]T.Y.Lin,N.Cercone(Eds.).Rough sets and data mining:analysis of imperfect data[M],Kluwer Academic Publishers,Boston,USA,1997.
[119]L.Polkowski,A.Skowron(Eds.).Rough sets in knowledge discovery 2:applications,case studies and software systems,studies in fuzziness and soft computing vol.19[M],Physica-Verlag,Heidelberg,1998.
[120]J.Wroblewski.Analyzing relational databases using rough set based methods [A],in:Eighth International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems IPMU.Madrid,Spain,vol.I[C],2000:256-262.
[121]T.P.Hong,L.H.Tseng,S.L.Wang.Learning rules from incomplete training examples by rough sets[J].Expert Systems with Applications,2002,22:285-293.
[122]Z.Pawlak.An inquiry into anatomy of conflicts[J].Journal of Information Sciences,1998,109:65-78.
[123]Z.Pawlak.Some remarks on conflict analysis[J].European Journal of Operational Research,2005,166:649-654.
[124]W.Z.Wu,W.X.Zhang,H.Z.Li.Knowledge acquisition in incomplete fuzzy information systems via the rough set approacch[J].Experts Systems,2003,20(5):280-286.
[125]Z.Pawlak.Rough set approach to knowledge-based decision support[J].European Journal of Operational Research,1997,99:48-57.
[126]W.Ziarko,J.Katzberg.Control algorithms acquisition,analysis and reduction:machine learning approach[A].in:Knowledge-Based Systems Diagnosis,Supervision and Control.Plenum Press,Oxford,1989:167-178.
[127] R. Slowinski, C. Zopounidis. Rough set sorting of firms according to bankruptcy risk [A], in: M. Paruccini (ed.). Applying Multiple Criteria Aid for Decision to Environmental Management. Kluwer, Dordrecht. Nether- lands, 1994, 339-357.
[128] D. Brindle. Speaker-independent speech recognition by rough sets analysis[A]. in: T. Y. Lin. (ed.). The Third International Workshop on Rough Sets and Soft Computing Proceedings (RSSC'94), San Jose State University, San Jose, California, USA, 1995: 85-88.
[129] S.K.M. Wong, W. Ziarko. On optimal decision rules in decision tables[D]. Bulletin of Polish Academy of Sciences, 1985, 33(11/12): 693-696.
[130] A. Skowron and C. Rauszer, The discernibility matrices and functions in information systems [A]. In: R. Slowinski, Editor, Intelligent Decision Support - Handbook of Applications and Advances of the Rough Sets Theory, System Theory, Knowledge Engineering and Problem Solving vol. 11[M], Kluwer Academic Publishers, Dordrecht, The Netherlands, 1992: 331-362.
[131] H. Tanaka et al. Fuzzy inference system based on rough sets and its application to Medical diagnosis [A]. Intelligent Decision Support-Handbook of Applications and Advances of Rough Sets Theory [M], Kluwer Academic PPublishers, Dordrecht, Boston, London, 1992: 111-118.
[132] G.I. Paterson et al. Rough classification of pneumonia patients using a clinical databases[A]. Rough Sets, Fuzzy Sets and Knowledge Discovery[M], Springerverge, 1994: 412-419.
[133] L.K. Woolery et al. Machine learning for an expert system to predict pretern birth risk[J]. Journal of the American Informatics Association, 1994, 1(6): 439-446.
[134] Z. Pawlak. Rough classification of patients after highly selective vagotomy for duodenal ulcer[J]. International Journal Man-Machine Studies, 1986, 24: 413-433.
[135]徐佩绅,华蕴博,陆金芳,等.中医经验的形式化和计算机辨证模型—泮澄濂肝病经验的程序设计[J].中国生物医学工程学报,1985,4(1):1-6.
[136]朱文锋.现代中医临床诊断学[M].北京:人民卫生出版社,2003.
[137]阮达,黄崇福.模糊集与模糊信息粒理论[M])北京:北京师范大学出版社,2000.
[138]朱咏华,朱文锋.中医症状的规范化研究[J].湖南中医学院学报,2002,22(3):35-37.
[139]朱文锋.证、症、征等词的概念与演变[J].科学术语研究,2003,5(4):20-21.
[140]朱文锋,黄碧群.证、证候的辨析与规范[J].山西中医,2005,21(3):1-3.
[141]胡金亮,李建生,余学庆.中医证候诊断标准研究背景与现状[J].河南中医学院学报,2005,20(3):77-79.
[142]施明辉,周昌乐.一种基于ANN的中医辨证不确定性推理模型研究[J].计算机工程与应用,2007,43(7):10-13,27.
[143]施明辉,周昌乐.一种用ANN实现带权不确定性推理的方法[J].哈尔滨工业大学学报,2007,39(9):1491-1495.
[144]王继成,吕维雪.一个基于符号神经网络的心电图分类系统[J].中国生物医学工程学报,1996,15(3):202-207.
[145]T.M.Mitchell.Machine learning[M].Mc Graw-Hill,Portland,1997.
[146]D.Michie,D.Spiegelhalter,C.C.Taloy.Machine learning,neural and statistical classification[M].Eills Horwood Limited,England,1994.
[147]J.R.Quinlan.C4.5-Programs for Machine Learning[M].Morgan Kaufmann,Palo Alto,1993.
[148]韩祯祥,张琦,文福拴.粗糙集理论及其应用综述[J].控制理论与应用,1999,16(2):153-165.
[149] P.S. Michalski, I. Mozetic, J. Hong, et. al. The multi-purpose incremental learning system AQ15 and its testing application to three medical domains [A], in: Proceedings of the Fifth National Conference on Artificial Intelli- gence[C], AAAI Press,Mento Park, 1986, 1041-1045.
[150] A. Skowron, J. Grzymala-Busse. From rough set theory to evidence theory [A]. in:R. Yager, M. Fedrizzi, J. Kacprzyk Eds. Advances in the Dempster-shafer Theory of Evidence[M], John Wiley&Sons, NY, 1994, 193-236.
[151] B. Walczak, D.L. Massart. Rough sets theory [J]. Chemometrics and Intelligent Laboratory Systems, 1999, (47): 1-16.
[152] M.H. Shi, C.L. Zhou. Diagnostic rules discovery with hierarchical clustering and focusing mechanism based on rough sets theory[A], Proceedings of the Fourth International Conference on Fuzzy System and Knowledge Discovery (FSKD 2007) [C]: 673-677.
[153] S. Tsumoto. Rule induction with grouping target concepts based on rough sets[R]. Electronic Notes in Theoretical Computer Science, 2003, 82(4): 1- 12. http://www.elsevier.nl/locate/entcs/volume82.html.
[154] ftp://ftp.ics.uci.edu/pub/machine—learning-databases/
[155] G.Y. Wang. Rough reduction in algebra view and information view[J], International Journal of Intelligent Systems, 2003, (18): 679-688.
[156] Z. Pawlak, A. Skowron. Rough sets and Boolean reasoning[J], Information Sciences, 2007, 177: 41-73.
[157] J.W. Grzymala-Busse. Data with missing attribute values: generalization of indiscernibility relation and rule reduction [A]. Transactions on Rough Sets I[M], LNCS 3100, Springer-Verlag, Berlin, 2004.
[158] M. Kryszkiewicz. Rough set approach to incomplete information systems [J]. Information Sciences, 112: 33-49.
[159]M.Kryszkiewicz.Rules in incomplete information systems[J].Information Sciences,1999,113:271-292.
[160]Y.Leung,W.Z.Wu,W.X.Zhang.Knowledge acquisition in incomplete information systems:a rough set approach[J],European Journal of Operational Research,2006,168:164-180.
[161]R.Latkowski.Flexible indiscernibility relations for missing attribute values [J].Fundamenta Informaticae,2005,67:131-147.
[162]X.Hu,N.Cercone.Data mining via discretization,generalization and rough set feature selection[J].Knowledge and Information Systems:An International Journal,1999,1(1):33-60.
[163]H.S.Nguyen.Discretization of real value attributes,boolean reasoning approach [D].Warsaw University,Warsaw,Poland,1997.
[164]H.S.Nguyen.From optimal hyperplanes to optimal decision trees[J].Fundamenta Informaticae,1998,34(1-2):145-174.
[165]J.W.Grzymala-Busse,W.Ziarko.Data mining and rough set theory[J].Communications of the ACM,2000,43:108-109.
[166]H.S.Nguyen.On efficient handling of continuous attributes in large data bases[J].Fundamenta Informaticae,2001,48(1):61-81.
© 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 |