[1]程子成,吴根秀,宋姝婷.基于融合信息熵性质的信任函数概率逼近[J].江西师范大学学报(自然科学版),2014,(05):534-538.
　CHENG Zi-cheng,WU Gen-xiu,SONG Shu-ting.A Probability Approximations of Belief Function Based on Fusion of the Properties of Information Entropy[J].Journal of Jiangxi Normal University:Natural Science Edition,2014,(05):534-538.
点击复制

基于融合信息熵性质的信任函数概率逼近()
分享到：

《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2014年05期
页码:
534-538
栏目:
出版日期:
2014-10-31

文章信息/Info

Title:
A Probability Approximations of Belief Function Based on Fusion of the Properties of Information Entropy
作者:
程子成;吴根秀;宋姝婷
江西师范大学数学与信息科学学院,江西 南昌,330022
Author(s):
CHENG Zi-cheng；WU Gen-xiu；SONG Shu-ting
关键词:
信息熵; D-S理论; Pignistic概率转换; 决策
Keywords:
information entropy; D-S theory; pignistic probability transformation; decision-making
分类号:
TP391
文献标志码:
A
摘要:
对信息熵的相关性质进行研究，在 Pignistic 概率转换方法的基础上融合信息熵的性质提出信任函数概率逐步逼近的新算法，决策者根据决策需要可以设定逼近阀值，以此确定是否继续逼近，达到降低决策风险的目的，最后通过算例说明新方法的优越性。
Abstract:
The correlation properties of the informations entropy are studied. Based on the conversion method of pig-nistic probability,the new algorithm of gradual approximation belief function is proposed by fusion on the nature of entropy. According to the need of decision makers can set the threshold,to determine whether to continue the algo-rithm,in order to reduce the risk of decision-making. Finally,the advantages of this method through examples are explained.

参考文献/References:

[1] 黄林颖,吴根秀,万宁文,等.信任函数的逼近 [J].江西师范大学学报; 自然科学版,2006,30(1):58-62.
[2] 蒋雯,吴翠翠,贾佳,等.D-S证据理论中的基本概率赋值转换概率方法研究 [J].西北工业大学学报,2013,31(2):295-299.
[3] 王万请,赵拥军,黄洁,等.基于不确定度的基本概率赋值概率转换方法 [J].控制与决策,2013,28(8):1214-1218.
[4] Smets P,Kennes R.The transferable belief model [J].Artifial Intelligence,1994,66(2):191-234.
[5] Sudano J J.The system probability information content(PIC)relationship to contributing components,combini-ng independent multi-source beliefs,hybrid and pedig-ree pignistic probabilities [C]∥2002 Proceedings of the Fifth International Conference on Information Fusion.IEEE,2002:1277-1283.
[6] Sudano J.Pignistic probability transforms for mixes of low-and high-probability events [J].Proc of Fusion,2001,2001:23-27.
[7] Dezert J,Smarandache F.A new probabilistic transforma-tion of belief mass assignment [C]∥2008 11th International Conference on Information Fusion.IEEE,2008:1-8.
[8] Sudano J,Martin L.Equivalence between belief theories and naive Bayesian fusion for systems with independent evidential data-Part I,the theory [J].Proc of Fusion,2003,2003:1239-1243.
[9] Sudano J J,Martin L.Yet another paradigm illustrating evidence fusion(YAPIEF)[C]∥2006 9th International Conference on Information Fusion.IEEE,2006:1-7.
[10] Cuzzolin F.On the properties of the Intersection proba-Bility [J].Submitted to the Annals of Mathematics and AI,2007(1):1-34.
[11] Han Deqiang,Dezert J,Han Chongzhao,et al.Is entropy enough to evaluate the probability transformation approach of belief function?[C]∥Proc of International Conference on Information Fusion.IEEE,2010:1-7.
[12] Zhou Zhe,Yang Chunjie,Wen Chenglin.Belief function to probability:A tradeoff between easy decision-making and high risk [C]∥2013 10th IEEE International Conference on Control and Automation(ICCA).IEEE,2013:233-237.
[13] 韩德强,杨艺,韩崇昭.D-S证据理论研究进展及相关问题探讨 [J].控制与决策,2014,29(1):1-7.
[14] 刁联旺,李勇智,杨静宁.D-S证据推理的决策问题 [J].计算机工程与应用,2003,39(33):82-84,97.

相似文献/References:

[1]赖邦城,吴根秀.基于信息熵的证据融合方法[J].江西师范大学学报(自然科学版),2012,(05):519.
　LAI Bang-cheng,WU Gen-xiu.The Evidence Combination Method Based on Information Entropy[J].Journal of Jiangxi Normal University:Natural Science Edition,2012,(05):519.
[2]张巍,聂进,滕少华.基于互信息的模糊决策树及其增量学习[J].江西师范大学学报(自然科学版),2014,(01):89.
　ZHANG Wei,NIE Jin,TENG Shao-hua.Fuzzy Decision Tree Based on Mutual Information and Incremental Learning[J].Journal of Jiangxi Normal University:Natural Science Edition,2014,(05):89.
[3]程艳,苗永春.高维数据流的聚类离群点检测算法研究[J].江西师范大学学报(自然科学版),2014,(05):449.
　CHENG Yan,MIAO Yong-chun.The Study on Clustering-Based Outlier Detection Algorithm for High-Dimensional Data Stream[J].Journal of Jiangxi Normal University:Natural Science Edition,2014,(05):449.

备注/Memo

备注/Memo:
江西省自然科学基金(20114BAB201038)；江西省教育厅科技计划(GJJ14244)
更新日期/Last Update: 1900-01-01