基于综合冲突度量的高冲突证据融合算法
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摘要
为提高冲突证据的合成效果,针对已有基于改进Jousselme证据距离函数的冲突度量算法进行研究,提出基于非包含度和改进Jousselme证据距离函数共同度量证据冲突的综合冲突度量算法,并依此计算证据权重;在此基础上基于焦元被其他证据中同一焦元的共同支持度对焦元可信度计算式进行改进,并将上述改进结果应用于优化两两证据合成顺序的局部冲突信息再分配算法中。将新算法和已有相关算法进行算例与仿真实验比较分析,结果表明,在证据之间存在高冲突情况下,所提新算法的正确目标识别概率高于已有其他证据融合算法,且对于一般证据,新算法也具有较满意的证据融合效果,新算法是一种具有较高灵活性和较强抗干扰性的证据融合算法。
In order to enhance the fusion effect of conflicting evidence,the existing conflict measure algorithm which improves Jousselme evidence distance function is studied,and a comprehensive conflict measure algorithm based on both noninclusive degree and improved Jousselme evidence distance function is proposed. And then the weight of each piece of evidence is calculated. On this basis,the calculation formula of the credibility of the focal element is improved by using the total support degree of the same focal element from different pieces of evidence,and the calculation result is applied to the local conflict information redistribution algorithm which optimizes the two-two evidence fusion sequence. The new algorithm is compared with the related algorithms by using examples and simulation experiments. The results show that the correct target recognition probability of the new algorithm proposed in this paper is higher than that of other related evidence fusion algorithms in the case of high conflict evidence,and the new algorithm also has satisfactory evidence fusion effect for the general evidence,and it is a kind of evidential fusion algorithm with high flexibility and strong anti-interference ability.
In order to enhance the fusion effect of conflicting evidence,the existing conflict measure algorithm which improves Jousselme evidence distance function is studied,and a comprehensive conflict measure algorithm based on both noninclusive degree and improved Jousselme evidence distance function is proposed. And then the weight of each piece of evidence is calculated. On this basis,the calculation formula of the credibility of the focal element is improved by using the total support degree of the same focal element from different pieces of evidence,and the calculation result is applied to the local conflict information redistribution algorithm which optimizes the two-two evidence fusion sequence. The new algorithm is compared with the related algorithms by using examples and simulation experiments. The results show that the correct target recognition probability of the new algorithm proposed in this paper is higher than that of other related evidence fusion algorithms in the case of high conflict evidence,and the new algorithm also has satisfactory evidence fusion effect for the general evidence,and it is a kind of evidential fusion algorithm with high flexibility and strong anti-interference ability.
引文
[1] SHAFER G. A mathematical theory of evidence turns 40[J]. International Journal of Approximate Reasoning,2016,79:7-25.
[2] LEUNG Y,JI N N,MA J H. An integrated information fusion approach based on the theory of evidence and group decision making[J]. Information Fusion,2013,14(4):410-422.
[3] DESTERCKE S,BURGER T. Toward an axiomatic definition of conflict between belief functions[J]. IEEE Transactions on Cybernetics,2013,43(2):585-596.
[4] BURGER T. Geometric views on conflicting mass functions:From distances to angles[J]. International Journal of Approximate Reasoning,2016,70:36-50.
[5] ZADEH L A. A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination[J]. AI magazine,1986,7(2):85.
[6]孟晨晨,肖建于,罗兰.基于相似系数和Jaccard系数矩阵的证据冲突度量方法[J].重庆邮电大学学报:自然科学版,2017,29(3):421-426.MENG C C,XIAO J Y,LUO L. Evidence conflict measurement method based on similarity coefficient and Jaccard matrix[J]. Journal of Chongqing University of Posts and Telecommunications:Natural Science Edition,2017,29(3):421-426.
[7] DENG Y. Generalized evidence theory[J]. Applied Intelligence,2015,43(3):530-543.
[8]权文,王晓丹,周进登,等.一种修正的Dubois-Prade证据推理组合规则[J].控制与决策,2012,27(1):139-142.QUAN W,WANG X D,ZHOU J D,et al. A modified Dubois-Prade combination rule of evidence theory[J].Control and Decision,2012,27(1):139-142.
[9]邓勇,施文康,朱振福.一种有效处理冲突证据的组合方法[J].红外与毫米波学报,2004,23(1):27-32.DENG Y,SHI W K,ZHU Z F. Efficient combination approach of conflict evidence[J]. Journal of Infrared and Millimeter Waves,2004,23(1):27-32.
[10]宋亚飞,王晓丹,雷蕾,等.基于信任度和虚假度的证据组合方法[J].通信学报,2015,36(5):104:1-6SONG Y F,WANG X D,LEI L,et al. Evidence combination based on the degree of credibility and falsity[J].Journal on Communications,2015,36(5):104:1-6.
[11] BI W H,ZHANG A,QI L H,et al. The method of measuring conflict evidence based on the modified probability distribution function and similarity measure[C]//Proceedings of the 32nd Chinese Control Conference. Xian,China:Conference Publication,2013,4698-4702.
[12] QIAN J,GUO X F,DENG Y. A novel method for combining conflicting evidences based on information entropy[J]. Applied Intelligence,2017,46(4):1-13.
[13]汤潮,蒋雯,陈运东,等.新不确定度量下的冲突证据融[J].系统工程理论与实践,2015,35(9):2394-2400TANG C,JIANG W,CHEN Y D,et al. Conflicting evidence combination based on a new measure method of uncertainty[J]. Systems Engineering-Theory&Practice,2015,35(9):2394-2400.
[14] SILVA L G D O,FILHO A T D A. A multicriteria approach for analysis of conflicts in evidence theory[J]. Information Science. 2016,346-347:275-285.
[15] HAN D Q,DENG Y,HAN C Z,et al. Weighted Evidence Combination Based on Distance of Evidence and Uncertainty Measure[J]. Journal of Infrared and Millimeter Waves,2011,30(5):396-400.
[16]曹洁,郭雷雷.一种基于局部冲突分配的证据组合规则[J].计算机应用研究,2013,30(7):2033-2035.CAO J,GUO L L. Evidence combination rule based on local conflict distribution strategy[J]. Application Research of Computers,2013,30(7):2033-2035.
[17] SMARANDACHE F,DEZERT J. On the consistency of PCR6 with the averaging rule and its application to probability estimation[C]//International Conference on Information Fusion. Istanbul,Turkey:IEEE,2013,22:1119-1126.
[18] MEZAI L,HACHOUF F. Score level fusion algorithm using Differential Evolution and Proportional Conflict Redistribution rule[C]//IEEE International Conference on Systems,Man, and Cybernetics. Budapest, Hungary:IEEE,2017:001051-001056.
[19]卢章平,李小蕾,袁浩,等.基于证据推理的设计方案多属性决策新方法[J].江苏大学学报:自然科学版,2014,35(5):552-557.LU Zhangping,LI Xiaolei,YUAN Hao,et al. New multi-attribute decision-making method of designing scheme based on evidential reasoning[J]. Journal of Jiangsu University:Natural Science Editions,2014,35(5):552-557.
[20]彭颖,胡增辉,沈怀荣.一种修正证据距离[J].电子与信息学报,2013,35(7):1624-1629.PENG Y,HU Z H,SHEN H R. A modified distance of evidence[J]. Journal of Electronics&Information Technology,2013,35(7):1624-1629.
[21]毛艺帆,张多林,王路.基于重合度的证据冲突度量方法[J].控制与决策,2017,32(2):293-298.MAO Y F,ZHANG D L,WANG L. Measurement of evidence conflict based on overlapping degree[J]. Control and Decision,2017,32(2):293-298.
[2] LEUNG Y,JI N N,MA J H. An integrated information fusion approach based on the theory of evidence and group decision making[J]. Information Fusion,2013,14(4):410-422.
[3] DESTERCKE S,BURGER T. Toward an axiomatic definition of conflict between belief functions[J]. IEEE Transactions on Cybernetics,2013,43(2):585-596.
[4] BURGER T. Geometric views on conflicting mass functions:From distances to angles[J]. International Journal of Approximate Reasoning,2016,70:36-50.
[5] ZADEH L A. A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination[J]. AI magazine,1986,7(2):85.
[6]孟晨晨,肖建于,罗兰.基于相似系数和Jaccard系数矩阵的证据冲突度量方法[J].重庆邮电大学学报:自然科学版,2017,29(3):421-426.MENG C C,XIAO J Y,LUO L. Evidence conflict measurement method based on similarity coefficient and Jaccard matrix[J]. Journal of Chongqing University of Posts and Telecommunications:Natural Science Edition,2017,29(3):421-426.
[7] DENG Y. Generalized evidence theory[J]. Applied Intelligence,2015,43(3):530-543.
[8]权文,王晓丹,周进登,等.一种修正的Dubois-Prade证据推理组合规则[J].控制与决策,2012,27(1):139-142.QUAN W,WANG X D,ZHOU J D,et al. A modified Dubois-Prade combination rule of evidence theory[J].Control and Decision,2012,27(1):139-142.
[9]邓勇,施文康,朱振福.一种有效处理冲突证据的组合方法[J].红外与毫米波学报,2004,23(1):27-32.DENG Y,SHI W K,ZHU Z F. Efficient combination approach of conflict evidence[J]. Journal of Infrared and Millimeter Waves,2004,23(1):27-32.
[10]宋亚飞,王晓丹,雷蕾,等.基于信任度和虚假度的证据组合方法[J].通信学报,2015,36(5):104:1-6SONG Y F,WANG X D,LEI L,et al. Evidence combination based on the degree of credibility and falsity[J].Journal on Communications,2015,36(5):104:1-6.
[11] BI W H,ZHANG A,QI L H,et al. The method of measuring conflict evidence based on the modified probability distribution function and similarity measure[C]//Proceedings of the 32nd Chinese Control Conference. Xian,China:Conference Publication,2013,4698-4702.
[12] QIAN J,GUO X F,DENG Y. A novel method for combining conflicting evidences based on information entropy[J]. Applied Intelligence,2017,46(4):1-13.
[13]汤潮,蒋雯,陈运东,等.新不确定度量下的冲突证据融[J].系统工程理论与实践,2015,35(9):2394-2400TANG C,JIANG W,CHEN Y D,et al. Conflicting evidence combination based on a new measure method of uncertainty[J]. Systems Engineering-Theory&Practice,2015,35(9):2394-2400.
[14] SILVA L G D O,FILHO A T D A. A multicriteria approach for analysis of conflicts in evidence theory[J]. Information Science. 2016,346-347:275-285.
[15] HAN D Q,DENG Y,HAN C Z,et al. Weighted Evidence Combination Based on Distance of Evidence and Uncertainty Measure[J]. Journal of Infrared and Millimeter Waves,2011,30(5):396-400.
[16]曹洁,郭雷雷.一种基于局部冲突分配的证据组合规则[J].计算机应用研究,2013,30(7):2033-2035.CAO J,GUO L L. Evidence combination rule based on local conflict distribution strategy[J]. Application Research of Computers,2013,30(7):2033-2035.
[17] SMARANDACHE F,DEZERT J. On the consistency of PCR6 with the averaging rule and its application to probability estimation[C]//International Conference on Information Fusion. Istanbul,Turkey:IEEE,2013,22:1119-1126.
[18] MEZAI L,HACHOUF F. Score level fusion algorithm using Differential Evolution and Proportional Conflict Redistribution rule[C]//IEEE International Conference on Systems,Man, and Cybernetics. Budapest, Hungary:IEEE,2017:001051-001056.
[19]卢章平,李小蕾,袁浩,等.基于证据推理的设计方案多属性决策新方法[J].江苏大学学报:自然科学版,2014,35(5):552-557.LU Zhangping,LI Xiaolei,YUAN Hao,et al. New multi-attribute decision-making method of designing scheme based on evidential reasoning[J]. Journal of Jiangsu University:Natural Science Editions,2014,35(5):552-557.
[20]彭颖,胡增辉,沈怀荣.一种修正证据距离[J].电子与信息学报,2013,35(7):1624-1629.PENG Y,HU Z H,SHEN H R. A modified distance of evidence[J]. Journal of Electronics&Information Technology,2013,35(7):1624-1629.
[21]毛艺帆,张多林,王路.基于重合度的证据冲突度量方法[J].控制与决策,2017,32(2):293-298.MAO Y F,ZHANG D L,WANG L. Measurement of evidence conflict based on overlapping degree[J]. Control and Decision,2017,32(2):293-298.