A method of conflictive evidence combination based on the Markov chain

被引：0

作者：

Li, Xin-De ^{[1
]}

Dong, Qing-Quan ^{[1
]}

Wang, Feng-Yu ^{[1
]}

Luo, Chao-Min ^{[2
]}

机构：

[1] Key Laboratory of Measurement and Control (School of Automation, Southeast University), Ministry of Education, Nanjing

[2] Department of Electrical and Computer Engineering, University of Detroit Mercy, Michigan

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2015年 / 41卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Combination rule; Conflict; Evidence reasoning; Markov chain; State-uncertainty;

D O I：

10.16383/j.aas.2015.c140681

中图分类号：

学科分类号：

摘要：

Aiming at the problem that highly conflictive evidence can not be processed by Dempster rule in intelligent information processing, a method of conflictive evidence combination based on Markov chain is proposed by considering the high-efficiency anti-interference performance for the sequentiality of sequential evidences. At first, the deterministic state description in the classic Markov chain is extended to nondeterministic state description. And then, the past evidences are sampled sequentially according to the sliding window width l, which could be amended according to the weight computed by utilizing the similarity measure. A Markov model is established on these past evidences amended so that a transition probability matrix could be obtained, which is used to compute the evidential representative. Finally, this representative is combined with itself for l-1 times according to the Murphy's combination method. Of course, this method also fits parallel fuse in a step. Through simulation experiments, the comparisive analysis show that the new method's advantage is obvious. That is to say, it efficiently solves the problem of the combination of conflictive evidences; moreover, it keeps robustness and sensibility of combinational result. Copyright © 2015 Acta Automatica Sinica. All rights reserved.

引用

页码：914 / 927

页数：13

共 34 条

[31]

Smets P., Decision making in the TBM: The necessity of the pignistic transformation, International Journal of Approximate Reasoning, 38, 2, pp. 133-147, (2005)

[32]

Murphy C.K., Combining belief functions when evidence conflicts, Decision Support Systems, 29, 1, pp. 1-9, (2000)

[33]

Li W.-L., Guo K.-H., Combination rules of D-S evidence theory and conflict problem, Systems Engineering-Theory & Practice, 30, 8, pp. 1422-1432, (2010)

[34]

Li X.-D., Wang F.-Y., A method of evidence reasoning based on ISODATA clustering and improved similarity measure, Acta Automatica Sinica, 41, 3, pp. 575-590, (2015)

← 1 2 3 4 →