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基于云模型的信息粒化 被引量:3

Information Granulating Based on Cloud Model
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摘要 粒计算作为一种新的信息和知识处理的方法近来已经被许多研究者所重视,在许多领域中得到应用。如何构建合理有效的信息粒是粒计算的前提。在简要阐述云模型理论和信息粒理论的基础上,给出基于云模型进行信息粒化的模型。 Granular computing is a new information processing method. Many models and methods ot granutar compuung have been proposed and studied. Nowadays,the granular computing has been appeared in many areas of information processing. Granules are regarded as the primitive notion of granular computing. It is a key issue how to construct information granules in granular computing. In this paper,on the basis of cloud theory and granular computing,a model of constructing information granules is proposed.
机构地区 南昌大学
出处 《现代电子技术》 2007年第4期98-99,102,共3页 Modern Electronics Technique
基金 江西省科技厅和江西省教育厅科技计划项目(赣教技字[2006]31号)
关键词 信息粒 粒计算 云模型 时间复杂性 information granules granular computing cloud model time complexity
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参考文献12

  • 1蒋嵘,李德毅,陈晖.基于云模型的时间序列预测[J].解放军理工大学学报(自然科学版),2000,1(5):13-18. 被引量:37
  • 2杨朝晖,李德毅.二维云模型及其在预测中的应用[J].计算机学报,1998,21(11):961-969. 被引量:127
  • 3扬帆.基于云模型的时间序列预测[D].南京:解放军理工大学,1999.
  • 4Witold Pedrycz. Granular Computing: An Introduction[J].IEEE,2001:1 349 - 1 354.
  • 5Zadeh L A. Fuzzy Sets and Information Granularity. In Advances in Fuzzy Sets Theory and Applications, North Holland, Amsterdam, 1979.3 - 18.
  • 6刘清.Rough 集及 Rough 推理[M].北京,科学出版社,2001.
  • 7刘清.学术报告,信息粒及粒计算的近似推理[A].2002.
  • 8Zadeh L A,Towards a Theory of Fuzzy Information granulation and Its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets and Systems , 1997,90(2) : 111 -127.
  • 9Lin T Y,Eric Louie. Modeling the Real World for Data Mining: Granular Computing Approach. IEEE, 2001, 3 044-3 049.
  • 10James Peters, Andrzej Skowron,Jaroslaw Stepaniuk. Information Granules in Spatial Reasoning. IEEE,2001:1 355 -1 360.

二级参考文献19

  • 1李德毅.发现状态空间理论[J].小型微型计算机系统,1994,15(11):1-6. 被引量:25
  • 2李德毅,孟海军,史雪梅.隶属云和隶属云发生器[J].计算机研究与发展,1995,32(6):15-20. 被引量:1261
  • 3[1]Francoise F, Samy B, Daniel C. On the prediction of solar activity using different neural network models [J/OL]. http://www.syntim.inria.fr/fractales, 1998-05-01.
  • 4[2]Claudio B, Wang X S, Sushil J, et al. Discovering frequent event patterns with multiple granularities in time sequences [J]. IEEE Transactions on Knowledge and Data Engineering, 1998,10(2):222-237.
  • 5[3]Park D C, El-Sharkawi M A, Marks R J. Electric load forecasting using an artificial neural network [J]. IEEE Transaction on Power Systems, 1991,6(2):442-449.
  • 6[4]Varfis A, Versino C. Univariate economic time-series forecasting by connectionist methods [C]. In Proceeding of the International Neural Network Conference (INNC), Paris, France, 1990:342-345.
  • 7[5]Dietterich T G, Michalski R S. Learning to predict sequences, machine learning [M]. An Artificial Intelligence Approach, 1986.
  • 8[6]Li D Y, Han J W, Shi X M, et al. Knowledge representation and discovery based on linguistic atoms [J]. Knowledge-Based System, 1998,10:431-440.
  • 9[7]Li D Y, Di K C, Li D R. Mining association with linguistic cloud models [C]. PAKDD'98. Proceeding of the Second Pacific-Asia Conf. on Knowledge Discovery & Data Mining Melibourne, Australia, Springer-Verlag Heidelberg, 1998.
  • 10[8]Li D Y, Shi X M, Paul W, et al. Soft inference mechanism based on cloud models [C]. Logic Programming and Soft Computing, Reach Studies Press, 1997.

共引文献156

同被引文献20

  • 1秦昆,李德毅,许凯.基于云模型的图像分割方法研究[J].测绘信息与工程,2006,31(5):3-5. 被引量:31
  • 2许凯,秦昆,裴韬.一种交互式的云模型图像分割方法[J].计算机工程与应用,2006,42(34):33-35. 被引量:8
  • 3DOUGLAS LAMBERT,等.Fundamentals of Logistics Management[M]. McGraw-Hill, 2003.
  • 4史忠植.知识工程和知识管理[M].北京:机械工业出版社,2003.
  • 5DOUGLAS LAMBERT,等.Fundamentals of Logistics Management[M].McGraw-Hill, 2003.
  • 6张铃;张钹.问题求解理论及应用一商空间粒度计算理论及应用[M]北京:清华大学出版社,2007.
  • 7苗夺谦;王国胤;刘清.粒计算:过去、现在与展望[M]北京:科学出版社,2007.
  • 8苗夺谦;李德毅;姚一豫.不确定性与粒计算[M]北京:科学出版社,2011.
  • 9王国胤;李德毅;姚一豫.云模型与粒计算[M]北京:科学出版社,2012.
  • 10李德毅;杜鷁.不确定性人工智能[M]北京:国防工业出版社,2005.

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