基于D-S证据理论的信息融合算法

摘要/Abstract

摘要： 针对现有D-S证据理论算法在信息融合应用中缺乏系统性的问题,提出了一种基于D-S证据理论的层次式融合算法。该算法模型采用多维属性信息的分域、层次融合方式,利用初始信息确定高层融合所需的概率分布的近似算法对数据进行融合处理,并对于可能存在的证据冲突问题,给出了算法的修正。仿真结果表明,该算法收敛速度快,准确度高,在低虚警率下具有较好的检测率。

关键词: 信息融合,D-S证据理论,层次式融合,近似计算,证据冲突

Abstract: Aiming at the problem on the insufficient in systematic in the application of information fusion using existed D-S evidence theory algorithm,this paper proposed a new hierarchical fusion algorithm based on D-S evidence theory．The model using hierarchical and domain fusion pattern processes the data which have many dimensions character．This paper also presented a new proximate calculation algorithm in order to resolve the problem of how to use the initial information to determine the probability in high level information fusion．And the algorithm gives the corrective algorithm in order to solve the problem of the conflict in evidence．The simulation results show that the algorithm can be in high probability of detection,and the probability of false alarm is low,the speed of convergence is fast and the correction is high.

Key words: Information fusion,D-S evidence theory,Hierarchical fusion,Proximate calculation,Evidence conflict

江涛. 基于D-S证据理论的信息融合算法[J]. 计算机科学, 2013, 40(Z11): 120-124. https://doi.org/

JIANG Tao. Information Fusion Algorithm Based on D-S Evidence Theory[J]. Computer Science, 2013, 40(Z11): 120-124. https://doi.org/

参考文献

[1] 张池平．多传感器信息融合方法及其在空间目标识别中的应用[D]．哈尔滨:哈尔滨工业大学,2006
[2] Findeisen R L,Allgower F,Foss B A．State and Output Feedback Nonlinear Model Predictive Control:An Overview[J]．European Journal of Control,2003(9):179-195
[3] 孙锐．基于D-S证据理论的信息融合及在可靠性数据处理中的应用研究[D]．成都:电子科技大学,2011
[4] 龚本刚．基于证据理论的不完全信息多属性决策方法研究[D]．合肥:中国科学技术大学,2007
[5] 蒋黎明,何佳浪,张宏．D-S证据理论中一种新的冲突证据融合方法[J]．计算机科学,2011,38(4):236-238
[6] Heeyoul C,Seungjin C,Yoonsuck C．Probabilistic Combination of Multiple Evidence[C]∥Proceedings of the 16^th International Conference on Neural Information Proceesing．Bangkok,Thailand,2009:302-311
[7] 徐琰珂,梁晓庚,贾晓洪．利用模糊证据理论的信息融合方法及其应用[J]．哈尔滨工业大学学报,2012,44(3):107-111
[8] Xu Xiao-bin,Wen Cheng-lin,Li Zhi-liang．A new method forconstructing fuzzy evidence based on the non-consonantrandom set[J]．Journal of electronics,2009,26(1):31-37
[9] 何小飞．基于贝叶斯网络和D-S证据理论的电网故障诊断模型研究[D]．成都:西南交通大学,2010
[10] Thorp J S,Phadke A G．Expose Hidden Failures to Prevent caseading outages[J]．IEEE TranSonpower Delive,2008,10:54-55
[11] 方阳．基于层次分析法和D-S证据理论的电信网网络安全风险评估模型的研究与应用[D]．北京:北京邮电大学,2010
[12] 李月,徐余法,陈国初,等．D-S 证据理论在多传感器故障诊断中的改进及应用[J]．东南大学学报,2011,41(9):102-106
[13] 李艳娜,乔秀全,李晓峰.基于证据理论的上下文本体建模以及不确定性推理方法[J]．电子与信息学报,2010,32(8):1806-1811
[14] 刘勇生．基于证据理论的网络安全风险组合评估方法[J]．计算机仿真,2012,29(1):106-109
[15] 肖文．基于证据理论的多属性决策关联问题研究[D]．南昌:江西财经大学,2010
[16] 黄蕊．下一代电信网安全评估指标体系的研究[D]．北京:北京邮电大学,2010
[17] Murphy C．Combining of Belief Functions When Evidence Conflicts [J]．Decision Support Systems,2000,29(1):1-9
[18] Han Li,Shi Li-ping．Approach to evidence combination based on rough set[C]∥International Conference on Electronic Computer Technology．Macau,China,2009:693-697
[19] Yager R．On the Dempster Shafer Framework and New Combination Rules[J]．Information Sciences,1987,41(2):93-137
[20] Dubois D,Prade H．Representation and Combination of Uncertainty with Belief Functions and Possibility Measures[J]．Computational Intelligence,1988,4(3):244-264
[21] Smarandaceh F,Dezert J．Proportional conflict redistributionrules for information fusion[M]．Smarandache F,Dezert J,eds．Vol.2．Rehoboth:American Research Press,2006:3-68
[22] 李建平．面向异构数据源的网络安全态势感知模型与方法研究[D]．哈尔滨:哈尔滨工业大学,2010
[23] Boujelben M A,Smet Y D,Frikha A,et al．Building abinary outranking relation in uncertain,imprecise and multi-experts contexts:The application of evidence theory [J].International Journal of Approximate Reasoning,2009,50(8):1259-1278
[24] Chin K S,Yang J B,Guo M,et al．Evidential-Reasoning-Interval-Based Method for New Producet Design Assessment[J]．IEEE Transaetions on Engineering Management,2009,56(1):142-156
[25] 张彦峰,何佩琨．一种改进的AdaBoost算法——M-Asy AdaBoost[J]．北京理工大学学报,2011,31(1):64-73
[26] 冷宣兵,王平,张立．证据理论合成准则的一种新算法及其验证[J]．计算机仿真,2010,27(2):162-165
[27] Moore A W,Zuev D．Internet traffic classification using bayesian analysis techniques[C]∥Internet Traffic Classification Using Bayesian Analysis Techniques in the Proceedings of the ACM SIGMETRICS．Banff,Canada,2005

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