一种新的证据冲突识别与调整方法

doi:10.16183/j.cnki.jsjtu.2021.118

摘要/Abstract

摘要：

针对现有方法在识别证据冲突方面存在的不足,结合传统证据冲突,提出一种证据冲突识别与调整的方法.该方法首先将传统证据冲突的加权平均值作为识别指标,其次引入可信度和不确定度,以是否为互异证据间冲突分两种情形确定权重系数.然后,结合传统证据冲突和Jousselme信息距离对识别出的证据冲突加以调整.该方法以传统证据冲突为基础,确保了识别指标的代表性和识别结果的真实性,此外,权重系数综合考虑证据内和证据间的可信度,也更具代表性.最后,选取降低程度和平均偏差分析验证了所提方法.研究结果表明,所提方法的识别精度较高,能够有效地调整证据冲突.

关键词: 证据冲突, 加权平均值, 综合可信度, 权重系数

Abstract:

Aimed at the shortcomings of existing methods in identifying evidence conflicts, in combination with traditional evidence conflicts, an evidence conflict identification and adjustment method is proposed. First, the weighted average of traditional evidence conflicts is used as the identification index in this method. Then, the credibility and uncertainty are introduced to determine the weight coefficients in two situations based on whether it is a conflict between different evidences. After that, the identified evidence conflicts are adjusted in combination with the traditional evidence conflicts and Jousselme information distance. This method is based on the traditional evidence conflicts, which ensures the representativeness of the identification index and the authenticity of the identification results. In addition, the weight coefficients comprehensively consider the credibility within and between the evidence, which is also more representative. Finally, the reduction degree and the average deviation are selected to analysis and verify the proposed method. The results show that the proposed method has a high recognition accuracy and can effectively adjust evidence conflicts.

Key words: evidence conflict, weighted average, comprehensive credibility, weight coefficient

中图分类号:

TP202

张鑫, 谈敏佳. 一种新的证据冲突识别与调整方法[J]. 上海交通大学学报, 2022, 56(8): 1043-1050.

ZHANG Xin, TAN Minjia. A Novel Method for Evidence Conflict Identification and Adjustment[J]. Journal of Shanghai Jiao Tong University, 2022, 56(8): 1043-1050.

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参考文献 23

[1]	DEMPSTER A P. Upper and lower probabilities induced by a multivalued mapping[J]. The Annals of Mathematical Statistics, 1967, 38(2): 325-339. doi: 10.1214/aoms/1177698950 URL
[2]	DEMPSTER A P. A generalization of Bayesian inference[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1968, 30(2): 205-232. doi: 10.1111/j.2517-6161.1968.tb00722.x URL
[3]	SHAFER G. A mathematical theory of evidence[M]. Princeton, NJ, USA: Princeton University Press, 1976.
[4]	LI Y Z, YAO S J, ZHANG R Y, et al. Analyzing host security using D-S evidence theory and multisource information fusion[J]. International Journal of Intelligent Systems, 2021, 36(2): 1053-1068. doi: 10.1002/int.22330 URL
[5]	YI L, JUN L, YA Q. Dynamic intuitionistic fuzzy multiattribute decision making based on evidential reasoning and MDIFWG operator[J]. Journal of Intelligent & Fuzzy Systems, 2019, 36(6): 5973-5987.
[6]	张扬, 杨建华, 侯宏. 基于证据聚类的水声目标识别算法研究[J]. 西北工业大学学报, 2018, 36(1): 96-102.
	ZHANG Yang, YANG Jianhua, HOU Hong. The underwater acoustic target recognition algorithm based on evidence clustering[J]. Journal of Northwestern Polytechnical University, 2018, 36(1): 96-102. doi: 10.1051/jnwpu/20183610096 URL
[7]	ZADEH L A. Reviews of books, a mathematical theory of evidence[J]. AI Magazine, 1984, 5(3): 81-83.
[8]	YAGER R R. On the dempster-shafer framework and new combination rules[J]. Information Sciences, 1987, 41(2): 93-137. doi: 10.1016/0020-0255(87)90007-7 URL
[9]	JIANG W, ZHAN J. A modified combination rule in generalized evidence theory[J]. Applied Intelligence, 2017, 46(3): 630-640. doi: 10.1007/s10489-016-0851-6 URL
[10]	李果, 马春阳, 马建晓. 基于DPCA和改进证据理论的融合式故障诊断[J]. 计算机工程与应用, 2019, 55(20): 197-201. doi: 10.3778/j.issn.1002-8331.1903-0365
	LI Guo, MA Chunyang, MA Jianxiao. Fusion fault diagnosis using DPCA & improved evidence theory[J]. Computer Engineering and Applications, 2019, 55(20): 197-201. doi: 10.3778/j.issn.1002-8331.1903-0365
[11]	LI H W, XIAO F Y. A method for combining conflicting evidences with improved distance function and Tsallis entropy[J]. International Journal of Intelligent Systems, 2020, 35(11): 1814-1830. doi: 10.1002/int.22273 URL
[12]	田明明, 叶继华, 万叶晶. 一种迭代修正的冲突证据改进方法[J]. 控制工程, 2021, 28(3): 565-570.
	TIAN Mingming, YE Jihua, WAN Yejing. An improved method of iterative correction to conflict evidence[J]. Control Engineering of China, 2021, 28(3): 565-570.
[13]	徐孙庆, 耿俊豹, 魏曙寰, 等. 一种改进的D-S冲突证据合成方法[J]. 火力与指挥控制, 2019, 44(10): 84-88.
	XU Sunqing, GENG Junbao, WEI Shuhuan, et al. An improved D-S conflicting evidence combination method[J]. Fire Control & Command Control, 2019, 44(10): 84-88.
[14]	李杨, 郭亚军. 一种基于不确定度的证据冲突识别方法[J]. 东北大学学报(自然科学版), 2017, 38(6): 892-897.
	LI Yang, GUO Yajun. A method of evidence conflict identification based on the uncertainty degree[J]. Journal of Northeastern University (Natural Science), 2017, 38(6): 892-897.
[15]	JOUSSELME A L, GRENIER D, BOSSÉ É. A new distance between two bodies of evidence[J]. Information Fusion, 2001, 2(2): 91-101. doi: 10.1016/S1566-2535(01)00026-4 URL
[16]	ALAM M, FERREIRA J, FONSECA J. Introduction to intelligent transportation systems[J]. Intelligent Transportation Systems, 2016, 52: 1-17.
[17]	彭鑫, 邓清勇, 田淑娟, 等. 多信道车联网V2R/V2V数据传输调度算法[J]. 通信学报, 2019, 40(3): 92-101.
	PENG Xin, DENG Qingyong, TIAN Shujuan, et al. Data dissemination scheduling algorithm for V2R/V2V in multi-channel VANET[J]. Journal on Communications, 2019, 40(3): 92-101.
[18]	王维, 包志华, 王慧玲, 等. 自适应负载状态的车联网信道接入算法[J]. 南京邮电大学学报(自然科学版), 2020, 40(2): 66-71.
	WANG Wei, BAO Zhihua, WANG Huiling, et al. Channel access algorithm in vehicle networks with adaptive load status[J]. Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition), 2020, 40(2): 66-71.
[19]	谭国真, 韩国栋, 张福新, 等. 基于网络效用最大化理论的分布式车联网拥塞控制策略[J]. 通信学报, 2019, 40(2): 82-91.
	TAN Guozhen, HAN Guodong, ZHANG Fuxin, et al. Distributed congestion control strategy using network utility maximization theory in VANET[J]. Journal on Communications, 2019, 40(2): 82-91.
[20]	WU G L, XU P P. Improving performance by a dynamic adaptive success-collision backoff algorithm for contention-based vehicular network[J]. IEEE Access, 2018, 6: 2496-2505. doi: 10.1109/ACCESS.2017.2783909 URL
[21]	杨建喜, 张悦, 刘博雅, 等. 车联网中基于D-S理论的GPSR改进算法[J]. 计算机工程与设计, 2019, 40(9): 2411-2415.
	YANG Jianxi, ZHANG Yue, LIU Boya, et al. Improved GPSR algorithm based on D-S theory in VANET[J]. Computer Engineering and Design, 2019, 40(9): 2411-2415.
[22]	周永筝, 邱恭安. 车联网中的交通安全信息证据理论融合算法[J]. 计算机工程与设计, 2017, 38(4): 874-877.
	ZHOU Yongzheng, QIU Gong'an. Evidence theory based traffic safety information fusion algorithm in Internet of vehicles[J]. Computer Engineering and Design, 2017, 38(4): 874-877.
[23]	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): 102-107.
	SONG Yafei, WANG Xiaodan, LEI Lei, et al. Evidence combination based on the degree of credibility and falsity[J]. Journal on Communications, 2015, 36(5): 102-107.

传统证据冲突	Jousselme 信息距离	新证据冲突	证据间差异度
k₁₁=0.043	d₁₁=0	K₁₁=0.005	ε₁₁=0
k₁₂=0.840	d₁₂=0.714	K₁₂=0.778	ε₁₂=0.188
k₁₃=0.862	d₁₃=0.756	K₁₃=0.807	ε₁₃=0.233
k₂₂=0.617	d₂₂=0	K₂₂=0.569	ε₂₂=0
k₂₃=0.601	d₂₃=0.154	K₂₃=0.532	ε₂₃=-0.188
k₃₃=0.539	d₃₃=0	K₃₃=0.447	ε₃₃=0

传统冲突	原始值	w'₁	w'₂	d	调整值
k₁₂	0.734	0.142	0.858	0.469	0.507
k₂₃	0.792	0.142	0.858	0.538	0.574
k₂₄	0.764	0.135	0.865	0.534	0.565
k₂₅	0.723	0.146	0.854	0.444	0.484
k₂₆	0.699	0.135	0.865	0.460	0.492
k₅₅	0.630	0.024	0.976	0	0.615

名称	O	F	I	G/%		W/%
名称	O	F	I	F-O	I-O	F-μ	I-μ
k₁₂	0.734	0.667	0.507	9.10	30.9	6.00	-19.4
k₂₃	0.792	0.725	0.574	8.40	27.4	15.2	-8.70
k₂₄	0.764	0.702	0.565	8.20	26.0	11.5	-10.2
k₂₅	0.723	0.654	0.484	9.50	33.0	3.90	-23.0
k₂₆	0.699	0.635	0.492	9.20	29.6	0.90	-21.8
k₅₅	0.630	0.600	0.615	4.80	2.40	-4.60	-2.30