基于信任度和确定度的证据加权组合方法

doi:10.11959/j.issn.1000-436x.2017010

Abstract

Abstract:

To overcome the invalidation problem of Dempster rule with high conflict,a weighted combination method based on degree of credibility and certainty was proposed.Firstly,the cosine similarity was modified to hold the ability to measure the evidence conflict when multi-subset focal elements were included,followed by the building of evidence credibility model.Secondly,the evidence certainty model based on precision and entropy was presented,which can both reflect the evidence’s degree of multi-subset focal elements and the dispersion degree of probability assignment.Then the weighted coefficient was determined by credibility and certainty.Finally,the normalized weighted coefficient was used to average the basic probability assignment,and the final combination result can be obtained according to Dempster rule.Numerical examples show that,compared with other traditional weighted combination methods,the proposed approach has made better performance in reducing conflict and accelerating convergence.

Key words: combination of evidences, credibility degree, certainty degree, conflict measurement, precision, entropy

CLC Number:

TP391

Lu WANG,Qing-hua XING,Yi-fan MAO. Weighted evidence combination based on degree of credibility and certainty[J]. Journal on Communications, 2017, 38(1): 83-88.

Figures/Tables 3

情形	BPA
情形1	m₁(θ₁)=m₁(θ₂)=0.5
情形1	m₂(θ₁)=m₂(θ₂)=0.5
情形2	m₁(θ₁)=m₁(θ₂)=0.5
情形2	m₂(θ₃)=m₂(θ₄)=0.5
情形3	$m_{1} (θ_{1}) = m_{1} (θ_{2}) = m_{1} (θ_{3}) = \frac{1}{3}$
情形3	$m_{2} (θ_{4}) = m_{2} (θ_{5}) = m_{2} (θ_{6}) = \frac{1}{3}$
情形4	m(θ₁)=1
情形4	m₂(θ₁θ₂)=m₂(θ₁θ₃)=0.5

References 23

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方法	m₁,m₂	m₁,m₂,m₃	m₁,m₂,m₃,m₄	m₁,m₂,m₃,m₄,m₅
	m(θ₁)=0	m(θ₁)=0	m(θ₁)=0	m(θ₁)=0
Dempster	m(θ₂)=0.857 1	m(θ₂)=0.631 6	m(θ₂)=0.328 8	m(θ₂)=0.140 4
	m(θ₃)=0.142 9	m(θ₃)=0.368 4	m(θ₃)=0.671 2	m(θ₃)=0.859 6
	m(θ₁)=0.154 3	m(θ₁)=0.556 8	m(θ₁)=0.865 3	m(θ₁)=0.968 8
	m(θ₂)=0.746 9	m(θ₂)=0.356 2	m(θ₂)=0.089 1	m(θ₂)=0.015 6
Murphy方法
	m(θ₃)=0.098 8	m(θ₃)=0.078 2	m(θ₃)=0.038 2	m(θ₃)=0.012 7
		m(θ₁θ₃)=0.008 8	m(θ₁θ₃)=0.007 4	m(θ₁θ₃)=0.002 9
	m(θ₁)=0.154 3	m(θ₁)=0.650 0	m(θ₁)=0.930 5	m(θ₁)=0.984 6
	m(θ₂)=0.746 9	m(θ₂)=0.254 7	m(θ₂)=0.027 4	m(θ₂)=0.002 4
Deng方法
	m(θ₃)=0.098 8	m(θ₃)=0.085 8	m(θ₃)=0.033 9	m(θ₃)=0.009 8
		m(θ₁θ₃)=0.009 5	m(θ₁θ₃)=0.008 2	m(θ₁θ₃)=0.003 2
	m(θ₁)=0.073 3	m(θ₁)=0.658 1	m(θ₁)=0.939 1	m(θ₁)=0.985 9
	m(θ₂)=0.862 3	m(θ₂)=0.240 9	m(θ₂)=0.019	m(θ₂)=0.001 4
本文方法
	m(θ₃)=0.064 4	m(θ₃)=0.093 7	m(θ₃)=0.034 2	m(θ₃)=0.009 6
		m(θ₁θ₃)=0.007 3	m(θ₁θ₃)=0.007 7	m(θ₁θ₃)=0.003 1

Weighted evidence combination based on degree of credibility and certainty

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