[1] ZADEH L A.A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination[J].AI Magazine,1986,2(7):85-90. [2] DUBIOS D, PRADE H. On the combination of evidence in various mathematical frameworks[M]. Reliability Data Collection and Analysis. Netherlands:Springer, 1992: 213-241. [3] DUBIOS D, PRADE H. Representation and combination of uncertainty with belief functions and possibility measures[J]. Computational Intelligence, 2010, 4(3): 244-264. [4] SMETS P. Data fusion in the transferable belief model[C]∥Proceedings of International Conference on Information Fusion. MD, US:IEEE,2002: PS21-PS33. [5] SMARANDACHE F, DEZERT J. Modified PCR rules of combination with degrees of intersections[C]∥Proceedings of International Conference on Information Fusion. Washington, DC, US:IEEE,2015: 2100-2107. [6] JIANG W. A correlation coefficient for belief functions[J]. International Journal of Approximate Reasoning, 2018, 103: 94-106. [7] MURPHY C K. Combining belief functions when evidence conflicts[J]. Decision Support Systems, 2000, 29(1):1-9. [8] DEZERT J, SAMARANDACHE F. Advances and applications of DSmT for information fusion Collected works [M]. 3th ed. Rehoboth, NM, US,: American Research Press, 2009. [9] LIU W. Analyzing the degree of conflict among belief functions[J]. Artificial Intelligence, 2006, 170(11):909-924.
[10] KHATIBI V, MONTAZER G A. A new evidential distance measure based on belief intervals[J]. Scientia Iranica,2010, 17(2): 119-132. [11] CHEN J, YE F, JIANG T, et al. Conflicting information fusion based on an improved DS combination method[J]. Symmetry, 2017, 9(11): 278-296. [12] 倪龙强,张丽华,姚新涛,等.一种基于粗糙集证据理论深度融合的局部冲突快速合成方法[J].兵工学报,2019,40(12):2560-2569. NI L Q,ZHANG L H,YAO X T, et al. A deep fusion method based on rough sets and evidence theory for local conflict evidence synthesis[J].Acta Armamentarii,2019,40(12):2560-2569. (in Chinese) [13] YU C, YANG J, YANG D, et al. An improved conflicting evidence combination approach based on a new supporting probability distance[J]. Expert Systems with Applications, 2015, 42(12): 5139-5149. [14] MO H, LU X, DENG Y. A generalized evidence distance[J].Journal of Systems Engineering & Electronics, 2016,27(2): 470-476. [15] JOUSSELME A L, GRENIER D, ELOIBOSSE. A new distance between two bodies of evidence[J]. Information Fusion,2001, 2(2): 91-101. [16] ZHOU D, PAN Q, CHHIPI-SHRESTHA G, et al. A new weighting factor in combining belief function[J]. PLoS ONE, 2017, 12(5): e0177695. [17] 赵静,关欣,衣晓,等.一种新的解决冲突问题的不确定性度量方法[J/OL].控制与决策:1-10[2019-07-09].https:∥doi.org/10.13195/j.kzyjc.2018.1184. ZHAO J, GUAN X, YI X,et al. A new uncertainty measurement method for conflict problem[J/OL]. Control and Decision: 1-10[2019-07-09].https:∥doi.org/10.13195/j.kzyjc.2018.1184.(in Chinese) [18] DENG Y. Deng entropy[J]. Chaos, Solitons & Fractals, 2016, 91: 549-553. [19] JIANG W, ZHUANG M, QIN X, et al. Conflicting evidence combination based on uncertainty measure and distance of evidence[J]. Springerplus, 2016, 5(1): 1217-1228. [20] PAN L, DENG Y. A new belief entropy to measure uncertainty of basic probability assignments based on belief function and plausibility function[J]. Entropy, 2018, 20(11):842-855. [21] YAGER R R. Interval valued entropies for Dempster-Shafer structures[J]. Knowledge-Based Systems, 2018, 161: 390-397.
[22] HOEHLE U. A remark on entropies with respect to plausibility measures[C]∥Proceedings of the 12th IEEE International Symposium on Multiple-Valued Logic. Vienna, Austria: IEEE, 1982:167-169. [23] JIROUSEK R, SHENOY P P. A new definition of entropy of belief functions in the Dempster-Shafer theory[J]. International Journal of Approximate Reasoning, 2018, 92: 49-65. [24] ABELLAN J. Analyzing properties of Deng entropy in the theory of evidence[J]. Chaos, Solitons & Fractals, 2017, 95: 195-199. [25] CUI H, LIU Q, ZHANG J, et al. An improved Deng entropy and its application in pattern recognition[J]. IEEE Access, 2019, 7: 18284-18292. [26] ZHOU D, TANG Y, JIANG W. An improved belief entropy and its application in decision-making[J]. Complexity, 2017, 2017: 1-15. [27] PAN Q, ZHOU D, TANG Y, et al. A novel belief entropy for measuring uncertainty in Dempster-Shafer evidence theory framework based on plausibility transformation and weighted hartley entropy[J]. Entropy, 2019, 21(2):163-182. [28] SUDANO J J. The system probability information content(PIC) relationship to contributing components, combining independent multi-source beliefs, hybrid and pedigree Pignistic probabilities[C]∥Proceedings of the International Conference on Information Fusion. Annapolis, MD,US:IEEE, 2002:1277-1283. [29] SUDANO J J. Yet another paradigm illustrating evidence fusion (YAPIEF)[C]∥Proceedings of the International Conference on Information Fusion. Florence, Italy:IEEE,2007:1-7. [30] SUDANO J J. Pignistic probability transforms for mixes of low-and-high-probability events[C]∥Proceedings of the 4th International Conference on Information Fusion. Montreal, Canada:IEEE, 2001:7-23. [31] 关欣,刘海桥,衣晓,等.基于反馈证据冲突度的概率转换[J].系统工程与电子技术,2018,40(7):1436-1442. GUAN X, LIU H Q, YI X, et al. Probability transformation based on the conflict degree of feedback evidence[J]. Systems Engineering and Electronics, 2018,40(7):1436-1442.(in Chinese) [32] LIU W. Analyzing the degree of conflict among belief functions[J]. Artificial Intelligence, 2006,170(11): 909-924. [33] 孙贵东,关欣,衣晓,等.冲突证据的相关系数度量方法[J].通信学报,2018,39(12):30-39. SUN G D, GUAN X, YI X, et al. Correlation coefficient measurement for conflict evidence[J]. Journal on Communications, 2018,39(12):30-39. (in Chinese)
第41卷第6期2020 年6月 兵工学报ACTA ARMAMENTARII Vol.41No.6Jun.2020
|