计算机科学 ›› 2014, Vol. 41 ›› Issue (7): 9-14.doi: 10.11896/j.issn.1002-137X.2014.07.002

• 综述 • 上一篇    下一篇

正态云概念的漂移性度量及分析

许昌林,王国胤   

  1. 西南交通大学信息科学与技术学院 成都610031;西南交通大学信息科学与技术学院 成都610031;重庆邮电大学计算智能重庆市重点实验室 重庆400065
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金项目(61272060),重庆市自然科学基金重点项目(2013jjB40003),计算智能重庆市重点实验室开放基金项目(CQ-LCI-2013-08)资助

Excursive Measurement and Analysis of Normal Cloud Concept

XU Chang-Lin and WANG Guo-yin   

  • Online:2018-11-14 Published:2018-11-14

摘要: 云模型把自然语言中定性概念的随机性和模糊性有机地综合在一起,通过正向云变换和逆向云变换实现了概念内涵与外延之间的相互转换。基于正态分布和高斯隶属函数的正态云具有普适性。文中通过正态云概念的外包络曲线,根据KL散度刻画分布函数之间差异的特性,定义了一种正态云概念的漂移性度量。最后,结合人类认知的特点,利用该方法以计算的方式对概念在认知过程中可能发生的漂移性进行了模拟研究和实验分析。

关键词: 正态云,正向云变换,逆向云变换,外包络曲线,概念漂移 中图法分类号TP181文献标识码A

Abstract: Cloud model integrates fuzziness and randomness of the qualitative concepts in the natural language,and rea-lizes the mutual transformation between concept’s intension and extension by forward cloud transformation and backward cloud transformation.The normal cloud based on normal distribution and Gaussian membership function has universality.In this paper,an excursive measurement based on outer envelope curve of normal cloud concept was defined by KL divergence,which is a non-symmetric measure of the difference between two probability distributions.Finally,based on the characteristics of human cognition,some simulation experiments were designed to study and analyze the excursion during the process of concepts’ cognition in a calculation way.

Key words: Normal cloud,Forward cloud transformation,Backward cloud transformation,Outer envelope curve,Concept excursion

[1] 张铃,张钹.问题求解理论及应用:商空间粒度计算理论及应用(第2版)[M].北京:清华大学出版社,2007
[2] 崔晓玲.对人类语言相似性和差异性根源的认知思考[J].延边大学学报:社会科学版,2001,34(3):85-87
[3] 李德毅,杜鹢.不确定性人工智能[M].北京:国防工业出版社,2005
[4] 李洪兴,汪培庄.模糊数学[M].北京:国防工业出版社,1994
[5] 李德毅,刘常昱.论正态云模型的普适性[J].中国工程科学,2004,6(8):28-34
[6] Li D Y,Liu C Y,Gan W Y.A new cognitive model:cloud model[J].Int.J.of Intelligent Systems,2009,24(3):357-375
[7] 刘常昱,冯芒,戴晓军,等.基于云X信息的逆向云新算法[J].系统仿真学报,2004,6(11):2417-2420
[8] 王立新.正态云的基本数学性质及云滤波器[J].个人通信,2011
[9] Wang G Y,Xu C L,Zhang Q H,et al.A multi-step backward cloud generator algorithm[C]∥The 8th International Confe-rence on Rough Sets and Current Trends in Computing(RSCTC’12).Chengdu,China,2012:313-322
[10] 许昌林,王国胤.实现稳定双向认知映射的逆向云变换算法[J].模式识别与人工智能,2013,26(7):634-642
[11] Kullback S,Leibler R A.On information and sufficiency[J].The Annals of Mathematical Statistics,1951,22(1):79-86
[12] Budka M,Gabrys B,Musial K.On accuracy of PDF divergence estimators and their applicability to representative data sampling[J].Entropy,2011,13:1229-1266
[13] Moreno P J,Ho P P,Vasconcelos N.A Kullback-Leibler divergence based kernel for SVM classification in multimedia applications[J].Advances in Neural Information Processing Systems,2004,16:1385-1392
[14] De Domenico M,Insolia A.Entropic approach to multiscale clustering analysis[J].Entropy,2012,14:865-879
[15] Contreras-Reyes J E,Arellano-Valle R B.Kullback-Leibler di-vergence measure for multivariate skew-normal distributions[J].Entropy,2012,14:1606-1626
[16] Villena S,Vega M,Babacan S D,et al.Using the Kullback-Leibler divergence to combine image priors in super-resolution image reconstruction[C]∥Proceedings of 2010IEEE 17th International Conference on Image Processing.Hong Kong,China,2010:893-896
[17] Jeffreys H.An invariant form for the prior probabili ty in estimation problems[J].Proceedings of the Royal Society A:Mathematical,Physical and Engineering Science,1946,6(1007):453-461
[18] 王国胤,许昌林,张清华,等.双向认知计算的p阶正态云模型的递归定义及分析[J].计算机学报,2013,36(11):2316-2329
[19] 王甦,汪安圣.认知心理学[M].北京:北京大学出版社,2006
[20] Lindsay P H,Norman D A.Human Information Processing:An Introduction to Psychology[M].New York:Academic Press,1977
[21] 张文修,徐伟华.基于粒计算的认知模型[J].工程数学学报,2007,24(6):957-971

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