An interval dempster-shafer approach

Lee, E.S.; Zhu, Qing

doi:10.1016/0898-1221(92)90157-d

Cited by 42 publications

(22 citation statements)

References 13 publications

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“…Yager 38 studied the combination and normalization of interval evidence too. Lee 39 also studied the combination of interval evidence. But their approaches for dealing with interval-valued belief structures are not really satisfied, which is illustrated by Wang.…”

Section: Er-based Nonlinear Programming Models For Computing Belief Dmentioning

confidence: 98%

Evidential reasoning-based nonlinear programming model for MCDA under fuzzy weights and utilities

Yang¹,

Liu²,

Zhou³

2010

Int. J. Intell. Syst.

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In a multiple-criteria decision analysis (MCDA) problem, qualitative information with subjective judgments of ambiguity is often provided by people, together with quantitative data that may also be imprecise or incomplete. There are several uncertainties that may be considered in an MCDA problem, such as fuzziness and ambiguity. The evidential reasoning (ER) approach is well suited for dealing with such MCDA problems and can generate comprehensive distributed assessments for different alternatives. Many researches in dealing with imprecise or uncertain belief structures have been conducted on the ER approach. In this paper, both triangular fuzzy weights of criteria and fuzzy utilities assigned to evaluation grades are introduced to the ER approach, which may be incurred in several circumstances such as group decision-making situation. The Hadamard multiplicative combination of judgment matrix is extended for the aggregation of triangular fuzzy judgment matrices, the result of which is applied as the fuzzy weights used in the fuzzy ER approach. The consistency of the aggregated triangular fuzzy judgment matrix is also proved. Several pairs of ER-based programming models are designed to generate the total fuzzy belief degrees and the overall expected fuzzy utilities for the comparison of alternatives. A numerical example is conducted to show the effectiveness of the proposed approach. C 2009 Wiley Periodicals, Inc.

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Section: Er-based Nonlinear Programming Models For Computing Belief Dmentioning

confidence: 98%

Evidential reasoning-based nonlinear programming model for MCDA under fuzzy weights and utilities

Yang¹,

Liu²,

Zhou³

2010

Int. J. Intell. Syst.

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“…The reason for doing so is to capture the true belief mass intervals of the combined focal elements 15 . Compared with existing combination and normalization approaches [24][25] …”

Section: Basic Of Ibsmentioning

confidence: 99%

Interval-valued Evidence Updating with Reliability and Sensitivity Analysis for Fault Diagnosis

Xu¹,

Zhang²,

Xu³

et al. 2016

IJCIS

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Information fusion methods based on Dempster-Shafer evidence theory (DST) have been widely used in fault diagnosis. In DST-based methods, the monitoring information collected from sensors is modeled as multiple pieces of diagnosis evidence in the form of basic belief assignment (BBA), and Dempster's rule is then used to combine these BBAs to obtain the fused BBA for diagnosis decision making. However, the belief structure with crisp singlevalued belief degrees in BBA may be too coarse to truthfully represent detailed fault information. Moreover, Dempster's rule only uses a static combination process, which is unsuitable for dynamically fusing information collected at different time steps. In order to address these issues, the paper proposes a dynamic diagnosis method based on interval-valued evidential updating. First of all, the diagnosis evidence is constructed as an interval-valued belief structure (IBS), which provides a more informative scheme than BBA to model fault information. Secondly, the proposed evidential updating strategy can generate updated IBS as global diagnosis evidence by updating the previous evidence with the new incoming evidence recursively. Thirdly, the reliability and sensitivity indices are designed to evaluate and compare the performance of the proposed updating strategy with other commonly used strategies. Finally, the effectiveness of the proposed evidential updating strategy is demonstrated through some typical fault experiments of a machine rotor.

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“…Assume M1 and M2 are two evidences, for combination of these evidences Equation (17) is used (Lee and Zhu, 1992):…”

Section: Interval Dempster-shafermentioning

confidence: 99%

“…In this study, opinions of multiple experts are used to assess the vulnerability of the statistical units where the experts' opinions are in the form of some intervals. In order to eliminate inconsistencies and uncertainties in the experts' opinions, Interval Dempster-Shafer combination rule (Lee and Zhu, 1992) has been used. After the fusion of the experts' opinions, information table of granular computing data is formed with minimum amount of inconsistency.…”

Section: Introductionmentioning

confidence: 99%

Physical Seismic Vulnerability Assessment of Tehran Using The Integration of Granular Computing and Interval Dempster- Shafer

Delavar

Bahrami

Zaré

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Several faults exist in the vicinity of Tehran, the capital of Iran such as North Tehran, Ray, Mosha and Kahrizak. One way to assist reducing the damage caused by the earthquake is the production of a seismic vulnerability map. The study area in this research is Tehran, based on the assumption of the activation of North Tehran fault. Degree of Physical seismic vulnerability caused by the earthquake depends on a number of criteria. In this study the intensity of the earthquake, land slope, numbers of buildings' floors as well as their materials are considered as the effective parameters. Hence, the production of the seismic vulnerability map is a multi criteria issue. In this problem, the main source of uncertainty is related to the experts' opinions regarding the seismic vulnerability of Tehran statistical units. The main objectives of this study are to exploit opinions of the experts, undertaking interval computation and interval Dempster-Shafer combination rule to reduce the uncertainty in the opinions of the experts and customizing granular computing to extract the rules and to produce Tehran physical seismic vulnerability map with a higher confidence. Among 3174 statistical units of Tehran, 150 units were randomly selected and using interval computation, their physical vulnerabilities were determined by the experts in earthquake-related fields. After the fusion of the experts' opinions using interval Dempster-Shafer, the information table is prepared as the input to granular computing and then rules are extracted with minimum inconsistency. Finally, the seismic physical vulnerability map of Tehran was produced with %72 accuracy.

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An interval dempster-shafer approach

Cited by 42 publications

References 13 publications

Evidential reasoning-based nonlinear programming model for MCDA under fuzzy weights and utilities

Evidential reasoning-based nonlinear programming model for MCDA under fuzzy weights and utilities

Interval-valued Evidence Updating with Reliability and Sensitivity Analysis for Fault Diagnosis

Physical Seismic Vulnerability Assessment of Tehran Using The Integration of Granular Computing and Interval Dempster- Shafer

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