Conflicting evidence combination of interval-valued belief structures

CHEN Sheng-qun, \ WANG Ying-ming

Systems Engineering - Theory & Practice ›› 2014, Vol. 34 ›› Issue (1) : 256-261.

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Systems Engineering - Theory & Practice ›› 2014, Vol. 34 ›› Issue (1) : 256-261. DOI: 10.12011/1000-6788(2014)1-256

Conflicting evidence combination of interval-valued belief structures

  • CHEN Sheng-qun1,2, \ WANG Ying-ming1
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Abstract

Dempster-Shafer evidence theory based on interval-valued belief structures has wide application prospect in the fields of information
fusion and decision analysis. To suppress the counterintuitive results generated from the combination of conflicting interval-valued belief
structures, a modified evidence combination approach is proposed. An optimization model of pignistic probability distance is built from the
global perspective to provide the relative importance weights for weighting evidence such that the weighted evidence can be reasonably combined with the
Dempster rule of combination. Numerical examples show the efficiency and rationality of the proposed approach.

Key words

evidence theory / interval-valued / pignistic probability distance / optimal combination

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CHEN Sheng-qun , \ WANG Ying-ming. Conflicting evidence combination of interval-valued belief structures. Systems Engineering - Theory & Practice, 2014, 34(1): 256-261 https://doi.org/10.12011/1000-6788(2014)1-256

References

[1] Wang Z Y, Marek-Sadowska M, Tsai K H, et al. Multiple fault diagnosis using n-detection tests[C]// Santa Barbra. IEEE Computer Society. Proceedings of the 21st International Conference on Computer Design, USA California University Press, 2003: 198-201.
[2] 周福娜,文成林,汤天浩,等.基于指定元分析的多故障诊断方法[J].自动化学报, 2009, 35(7): 971-982.Zhou F N, Wen C L, Tang T H, et al. DCA based multiple faults diagnosis method[J]. Acta Automatica Sinica, 2009, 35(7): 971-982.
[3] Riahi-Belkaoui A. Intellectual capital and firm performance of us multinational firms[J]. Journal of Intellectual Capital, 2003, 4(2): 215-226.
[4] Xu B, Feng K M. Determine the masking fault sets in complex systems[C]// IEEE Aerospace and Electronic System Society. IEEE Autotestcon Proceedings, USA Anaheim: IEEE Press, 2009: 585-590.
[5] 陈世杰,连可,王厚军.采用多信号流图模型的雷达接收机故障诊断方法[J].电子科技大学学报, 2009, 38(1): 87-91.Chen S J, Lian K, Wang H J. Fault diagnosis method of radar receiver using multi-signal flow graphs model[J]. Journal of University of Electronic Science and Technology of China, 2009, 38(1): 87-91.
[6] 连可,龙兵,王厚军.基于贝叶斯最大后验概率准则的大型复杂系统故障诊断方法研究[J].兵工学报, 2008, 29(3): 352-356.Lian K, Long B, Wang H J. A fault diagnosis approach of the large complex system based on Bayes theory[J]. Acta Armamentarii, 2008, 29(3): 352-356.
[7] 方甲永,肖明清,王学奇,等.测试不可靠条件下多故障诊断方法[J].北京航空航天大学学报, 2011, 37(4): 433-437.Fang J Y, Xiao M Q, Wang X Q, et al. Multiple fault diagnosis method with unreliable test[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(4): 433-437.
[8] Kennedy J, Eberhart R C. A discrete binary version of the particle swarm algorithm[C]// Proceedings of the World Multiconference on Systemic, Cybernetics and Informatics, Piscataway, NJ, 1997: 4104-4109.
[9] Eberhart R C, Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization[C]// Proceedings of the 2000 Congress on Evolutionary Computation, San Diego, 2000: 84-88.
[10] 蒋荣华,王厚军,龙兵. 基于DPSO的改进AO*算法在大型复杂电子系统最优序贯测试中的应用[J].计算机学报, 2008, 31(10): 1835-1838.Jiang R H, Wang H J, Long B. Applying improved AO* based on DPSO algorithm in the optimal test-sequencing problem of large-scale complicated electronic system[J]. Chinese Journal of Computers, 2008, 31(10): 1835-1838.
[11] Trelea I C. The particle swarm optimization algorithm: Convergence analysis and parameter selection[J]. Information Processing Letters, 2003, 85(6): 317-325.
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