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一种基于ISODATA聚类和改进相似度的证据推理方法

DOI: 10.16383/j.aas.2015.c140543, PP. 575-590

Keywords: 证据推理,冲突,聚类,相似测度,组合规则

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Abstract:

?针对智能信息处理中Dempster组合规则不能处理高度冲突的问题,从内、外证据不确定性分析的角度深入揭示了证据冲突产生的原因,即证据的冲突性不仅仅根源于证据间的矛盾,也与证据自身的不确定性密切相关,提出了一种同时考虑证据自冲突和外部冲突的相似性测度,然后利用新测度计算证据的众信度,对证据源进行修正;与此同时,根据原始证据间的聚类特性,利用迭代自组织数据分析技术(Iterativeselforganizingdataanalysistechniquesalgorithm,ISODATA)聚类方法进行聚类,然后利用Dempster组合规则合成每一聚类中所有证据为证据代表,并综合众信度和证据在该聚类的频度计算可靠度,最后,利用统一组合规则合成证据代表.并通过大量的算例,同其他方法和自身改进前后进行深入比较,优势比较明显,有效地解决了冲突证据合成出现的问题.

References

[1]  Shafer G. A Mathematical Theory of Evidence. Princeton: Princeton University Press, 1976.
[2]  Zadeh L A. Review of books: a mathematical theory of evidence. AI Magazine, 1984, 5(3): 81-83
[3]  Haenni R. Are alternatives to Dempster's rule of combination real alternatives. Information Fusion, 2002, 3(4): 237-239
[4]  Bian Zhao-Qi, Zhang Xue-Gong. Pattern Recognition. Beijing: Tsinghua University Press, 2000. (边肇祺, 张学工. 模式识别. 北京: 清华大学出版社, 2000.)
[5]  Martin A, Osswald C. Toward a combination rule to deal with partial conflict and specificity in belief functions theory. In: Proceedings of the 10th International Conference on Information Fusion. Quebec, Canada: IEEE, 2007. 1-8
[6]  Lefevre E, Colot O, Vannoorenberghe P. Belief function combination and conflict management. Information Fusion, 2002, 3(2): 149-162
[7]  Yager R R. On the D-S framework and new combination rules. Information Sciences, 1987, 41(2): 93-138
[8]  Liu W R. Analyzing the degree of conflict among belief functions. Artificial Intelligence, 2006, 170(11): 909-924
[9]  Wang Dong, Li Qi, Jiang Wen, Xu Xiao-Bin, Deng Yong. New method to combine conflict evidence based on pignistic probability distance. Infrared and Laser Engineering, 2009, 38(1): 149-154 (王栋, 李齐, 蒋雯, 徐晓滨, 邓勇. 基于pignistic概率距离的冲突证据合成方法. 红外与激光工程, 2009, 38(1): 149-154)
[10]  Deng Yong, Wang Dong, Li Qi, Zhang Ya-Juan. A new method to analyze evidence conflict. Control Theory & Applications, 2011, 28(6): 839-844(邓勇, 王栋, 李齐, 章雅娟. 一种新的证据冲突分析方法. 控制理论与应用, 2011, 28(6): 839-844)
[11]  Quan Wen, Wang Xiao-Dan, Wang Jian, Zhang Yu-Xi. New combination rule of DST based on local conflict distribution strategy. Acta Electronica Sinica, 2012, 40(9): 1880-1884(权文, 王晓丹, 王坚, 张玉玺. 一种基于局部冲突分配的DST组合规则. 电子学报, 2012, 40(9): 1880-1884)
[12]  He Bing, Hu Hong-Li. A modified DS evidence combination strategy. Acta Aeronautica et Astronautica Sinica, 2003, 24(6): 559-562(何兵, 胡红丽. 一种修正的DS 证据融合策略. 航空学报, 2003, 24(6): 559-562)
[13]  Yang J B, Xu D L. Evidential reasoning rule for evidence combination. Artificial Intelligence, 2013, 205: 1-29
[14]  Smets P. Analyzing the combination of conflicting belief functions. Information Fusion, 2007, 8(4): 387-412
[15]  Han De-Qiang, Han Chong-Zhao, Deng Yong, Yang Yi. Weighted combination of conflicting evidence based on evidence variance. Acta Electronica Sinica, 2011, 39(3A): 153-157(韩德强, 韩崇昭, 邓勇, 杨艺. 基于证据方差的加权证据组合. 电子学报, 2011, 39(3A): 153-157)
[16]  Hu Li-Fang, Guan Xin, Deng Yong, He You. Cause-analysis for conflicting evidences in the generalized power space. Control Theory and Applications, 2011, 28(12): 1717-1722(胡丽芳, 关欣, 邓勇, 何友. 广义幂集空间中证据冲突的原因分析. 控制理论与应用, 2011, 28(12): 1717-1722)
[17]  Smarandache F, Dezert J. Advances and Applications of DSmT for Information Fusion. Rehoboth, USA: American Research Press, Vol.2, 2006
[18]  Xiong Yan-Ming, Yang Zhan-Ping, Qu Xin-Fen. Novel combination method of conflict evidence based on evidential model modification. Control and Decision, 2011, 26(6): 883-887(熊彦铭, 杨战平, 屈新芬. 基于模型修正的冲突证据组合新方法. 控制与决策, 2011, 26(6): 883-887)
[19]  Lu Zheng-Cai, Qin Zheng. General framework for evidence combination and its approach to highly conflicting evidence fusion. Journal of Tsinghua University (Science and Technology), 2011, 51(11): 1701-1705, 1716(卢正才, 覃征. 证据合成的一般框架及高度冲突证据合成方法. 清华大学学报(自然科学版), 2011, 51(11): 1701-1705, 1716)
[20]  Li Wen-Li, Guo Kai-Hong. Combination rules of D-S evidence theory and conflict problem. Systems Engineering-Theory and Practice, 2010, 30(8): 1422-1432(李文立, 郭凯红. D-S 证据理论合成规则及冲突问题. 系统工程理论与实践, 2010, 30(8): 1422-1432)
[21]  Wang Lian-Feng, Song Jian-She, Zhu Yu, Cao Ji-Ping. Evidence combination based on fuzzy clustering analysis. Systems Engineering and Electronics, 2013, 35(1): 113-119(王连锋, 宋建社, 朱昱, 曹继平. 基于模糊聚类分析的证据组合. 系统工程与电子技术, 2013, 35(1): 113-119)
[22]  Quan Wen, Wang Xiao-Dan, Shi Zhao-Hui. Conflict evidence combination strategy based on evidence classification. Journal of Air Force Engineering University (Natural Science), 2012, 13(3): 80-84(权文, 王晓丹, 史朝辉. 基于证据分类的冲突证据合成方法. 空军工程大学学报(自然科学版), 2012, 13(3): 80-84)
[23]  Wang Jin-Hua, Wu Di, Cao Jie, Li Jun. Weighted combination of conflicting evidence based on evidence classification. Computer Science, 2013, 40(1): 247-250(王进花, 吴迪, 曹洁, 李军. 基于证据分类的加权冲突证据组合. 计算机科学, 2013, 40(1): 247-250)
[24]  Jousselme A L, Maupin P. Distances in evidence theory: comprehensive survey and generalizations. International Journal of Approximate Reasoning, 2012, 53(2): 118-145
[25]  Jousselme A L, Grenier D, Bossé é. A new distance between two bodies of evidence. Information Fusion, 2001, 2(2): 91-101
[26]  Li X, Dezert J, Smarandache F. Evidence supporting measure of similarity for reducing the complexity in information fusion. Information Sciences, 2011, 181(10): 1818-1835
[27]  Smets P. Data fusion in the transferable belief model. In: Proceedings of the 3rd International Conference on Information Fusion. Paris, France: IEEE, 2000. 21-33
[28]  Wen C L, Wang Y C, Xu X B. Fuzzy information fusion algorithm of fault diagnosis based on similarity measure of evidence. In: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II. Berlin, Heidelberg: Springer, 2008. 506-515
[29]  Liu Z, Dezert J, Pan Q, Mercier G. Combination of sources of evidence with different discounting factors based on a new dissimilarity measure. Decision Support Systems, 2011, 52(1): 133-141
[30]  Gu Zhao-Sheng. The Research and Application of Compositive Mathematical Methods to Water Quality Analysis, Simulation and Forecast in Reservoirs and Lakes [Ph.D. dissertation], Jilin University, China, 2006. (谷照升. 水库湖泊水质分析、模拟与预测的综合数学方法及其应用[博士学位论文]. 吉林大学, 中国, 2006.)
[31]  Sun Quan, Ye Xiu-Qing, Gu Wei-Kang. A new combination rules of evidence theory. Acta Electronica Sinica, 2000, 28(8): 117-119(孙全, 叶秀清, 顾伟康. 一种新的基于证据理论的合成公式. 电子学报, 2000, 28(8): 117-119)
[32]  Deng Yong, Shi Wen-Kang. A modified combination rule of evidence theory. Journal of Shanghai Jiaotong University, 2003, 37(8): 1275-1278(邓勇, 施文康. 一种改进的证据推理组合规则. 上海交通大学学报, 2003, 37(8): 1275-1278)
[33]  Li Bi-Cheng, Wang Bo, Wei Jun, Qian Ceng-Bo, Huang Yu-Qi. An efficient combination rule of evidence theory. Journal of Data Acquisition and Processing, 2002, 17(1): 33-36(李弼程, 王波, 魏俊, 钱曾波, 黄玉琪. 一种有效的证据理论合成公式. 数据采集与处理, 2002, 17(1): 33-36)
[34]  Li Xin-De, Dezert J, Huang Xin-Han, Meng Zheng-Da, Wu Xue-Jian. A fast approximate reasoning method in hierarchical DSmT (A). Acta Electronica Sinica, 2010, 38(11): 2566-2572(李新德, Dezert J, 黄心汉, 孟正大, 吴雪建. 一种快速分层递阶DSmT近似推理融合方法(A). 电子学报, 2010, 38(11): 2566-2572)
[35]  Li Xin-De, Yang Wei-Dong, Wu Xue-Jian, Dezert J. A fast approximate reasoning method in hierarchical DSmT (B). Acta Electronica Sinica, 2011, 39(3A): 31-36(李新德, 杨伟东, 吴雪建, Dezert J. 一种快速分层递阶DSmT近似推理融合方法(B). 电子学报, 2011, 39(3A): 31-36)
[36]  Li Xin-De, Yang Wei-Dong, Dezert J. An airplane image target's Multi-feature fusion recognition method. Acta Automatica Sinica, 2012, 38(8): 1298-1307(李新德, 杨伟东, Dezert J. 一种飞机图像目标多特征信息融合识别方法. 自动化学报, 2012, 38(8): 1298-1307)
[37]  Li Xin-De, Pan Jin-Dong, Dezert J. A target recognition algorithm of sequential aircrafts based on DSmT and HMM. Acta Automatica Sinica, 2014, 40(12): 2862-2876 (李新德, 潘锦东, Dezert J. 一种基于DSmT和HMM的序列飞机目标识别算法. 自动化学报, 2014, 40(12): 2862-2876)

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