Modified Interval Dempster-Shafer Theory and its Application for Target Identification

2012 ◽  
Vol 190-191 ◽  
pp. 1153-1156
Author(s):  
Hai Jun Liu ◽  
Hong Shan Nie ◽  
Hong Qi Yu ◽  
Hong Hu Hua ◽  
Zheng Liu
Keyword(s):  
Measurement Uncertainty ◽  
Target Identification ◽  
Identification Algorithm ◽  
Combination Rules ◽  
Feature Measurement ◽  
Dempster Shafer Theory ◽  
Evidence Combination ◽  
Shafer Theory

To deal with the problem of target identification caused by the achieved reliability of multi sensors and the feature measurement uncertainty of the target, this paper proposes a new identification algorithm based on Modified Interval Dempster-Shafer Theory (MIDST), which models sensor’s reliability as scalar value and identification outputs of each sensor as interval values, and then combines the actual interval outputs through interval evidence combination rules. At last, one simulation is presented to demonstrate the identification capability of the MIDST algorithm.

Neutrosophic Combination Rules Based on Dempster-Shafer Theory and Dezert-Smarandache Theory

2008 ◽  
Vol 7 (7) ◽  
pp. 1023-1029
Author(s):  
Jian Gao ◽  
Xinhan Huang ◽  
Min Wang ◽  
Xinde Li
Keyword(s):  
Combination Rules ◽  
Dempster Shafer Theory ◽  
Shafer Theory

One Fusion Approach of Fault Diagnosis Based on Rough Sets Theory and Dempster-Shafer Theory

2012 ◽  
Vol 241-244 ◽  
pp. 405-409 ◽  
Author(s):  
Yan Qin Su ◽  
Ji Hong Cheng ◽  
Ting Xue Xu
Keyword(s):  
Fault Diagnosis ◽  
Rough Sets ◽  
Decision Table ◽  
Combination Rules ◽  
Rough Sets Theory ◽  
Dempster Shafer Theory ◽  
Basic Probability ◽  
Shafer Theory ◽  
Sets Theory ◽  
Fusion Approach

There is the advantage of Rough Sets Theory for redundant condition reduction and D-S Theory for combination rules reasoning, one fusion approach based on the two theories was given. Firstly, the test data was discretizated and attribution reduced to get the reduction decision table. Then, the basic probability assignment was gotten through calculating the condition attribution of the decision table while the condition attribution was regarded as evidence input and the decision attribution as discernment frame. Finally, the evidence was combination reasoned and the fault diagnosis results were gotten, and the application example was verified its validity.

scholarly journals A novel weighted evidence combination rule based on improved entropy function with a diagnosis application

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771882399 ◽  
Author(s):  
Lei Chen ◽  
Ling Diao ◽  
Jun Sang
Keyword(s):  
Medical Diagnosis ◽  
Real Life ◽  
Entropy Function ◽  
New Method ◽  
Combination Rule ◽  
Initial Weight ◽  
Rule Based ◽  
Dempster Shafer Theory ◽  
Evidence Combination ◽  
Shafer Theory

Managing conflict in Dempster–Shafer theory is a popular topic. In this article, we propose a novel weighted evidence combination rule based on improved entropy function. This newly proposed approach can be mainly divided into two steps. First, the initial weight will be determined on the basis of the distance of evidence. Then, this initial weight will be modified using improved entropy function. This new method converges faster when handling high conflicting evidences and greatly reduces uncertainty of decisions, which can be demonstrated by a numerical example where the belief degree is raised up to 0.9939 when five evidences are in conflict, an application in faulty diagnosis where belief degree is increased hugely from 0.8899 to 0.9416 when compared with our previous works, and a real-life medical diagnosis application where the uncertainty of decision is reduced to nearly 0 and the belief degree is raised up to 0.9989.

scholarly journals Weighted Evidence Combination Rule Based on Evidence Distance and Uncertainty Measure: An Application in Fault Diagnosis

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Lei Chen ◽  
Ling Diao ◽  
Jun Sang
Keyword(s):  
Fault Diagnosis ◽  
Conflict Management ◽  
Information Fusion ◽  
Uncertainty Measure ◽  
Combination Rule ◽  
Rule Based ◽  
Dempster Shafer Theory ◽  
Evidence Combination ◽  
Weight Value ◽  
Shafer Theory

Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a novel weighted evidence combination rule based on evidence distance and uncertainty measure is proposed. The proposed approach consists of two steps. First, the weight is determined based on the evidence distance. Then, the weight value obtained in first step is modified by taking advantage of uncertainty. Our proposed method can efficiently handle high conflicting evidences with better performance of convergence. A numerical example and an application based on sensor fusion in fault diagnosis are given to demonstrate the efficiency of our proposed method.

Expert finding by the Dempster-Shafer theory for evidence combination

2017 ◽  
Vol 35 (1) ◽  
pp. e12231 ◽  
Author(s):  
Nafiseh Torkzadeh Mahani ◽  
Mostafa Dehghani ◽  
Maryam S. Mirian ◽  
Azadeh Shakery ◽  
Khalil Taheri
Keyword(s):  
Expert Finding ◽  
Dempster Shafer Theory ◽  
Evidence Combination ◽  
Shafer Theory

A Comparative Study of Evidence Theories in the Modeling, Analysis, and Design of Engineering Systems

2013 ◽  
Vol 135 (6) ◽  
Author(s):  
S. S. Rao ◽  
K. K. Annamdas
Keyword(s):  
Evidence Theory ◽  
Engineering Systems ◽  
Combination Rule ◽  
Multiple Sources ◽  
Combination Rules ◽  
Analysis And Design ◽  
Modeling Analysis ◽  
Evidence Combination ◽  
Different Types ◽  
Shafer Theory

The application of different types of evidence theories in the modeling, analysis and design of engineering systems is explored. In most studies dealing with evidence theory, the Dempster–Shafer theory (DST) has been used as the framework not only for the characterization and representation of uncertainty but also for combining evidence. The versatility of the theory is the motivation for selecting DST to represent and combine different types of evidence obtained from multiple sources. In this work, five evidence combination rules, namely, Dempster–Shafer, Yager, Inagaki, Zhang, and Murrphy combination rules, are considered. The limitations and sensitivity of the DST rule in the case of conflicting evidence are illustrated with examples. The application of all the five evidence combination rules for the modeling, analysis and design of engineering systems is illustrated using a power plant failure example and a welded beam problem. The aim is to understand the basic characteristics of each rule and develop preliminary guidelines or criteria for selecting an evidence combination rule that is most appropriate based on the nature and characteristics of the available evidence. Since this work is the first one aimed at developing the guidelines or criteria for selecting the most suitable evidence combination rule, further studies are required to refine the guidelines and criteria developed in this work.

Decision Support System for Advanced Traffic Management Through Data Fusion

2002 ◽  
Vol 1804 (1) ◽  
pp. 173-178 ◽  
Author(s):  
Lawrence A. Klein ◽  
Ping Yi ◽  
Hualiang Teng
Keyword(s):  
Data Fusion ◽  
Traffic Management ◽  
Information Sources ◽  
Partial Information ◽  
Weather Conditions ◽  
Field Testing ◽  
Traffic Operations ◽  
Dempster Shafer Theory ◽  
Classification Technique ◽  
Shafer Theory

The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule is applied to determine the most probable event—as that with the largest probability based on the information obtained from all contributing sources. The Dempster–Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.

scholarly journals Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3727
Author(s):  
Joel Dunham ◽  
Eric Johnson ◽  
Eric Feron ◽  
Brian German
Keyword(s):  
Sensor Fusion ◽  
A Priori ◽  
Evidential Reasoning ◽  
Unmanned Aerial Systems ◽  
Sufficient Information ◽  
User Input ◽  
Transition Potential ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Aerial Systems

Sensor fusion is a topic central to aerospace engineering and is particularly applicable to unmanned aerial systems (UAS). Evidential Reasoning, also known as Dempster-Shafer theory, is used heavily in sensor fusion for detection classification. High computing requirements typically limit use on small UAS platforms. Valuation networks, the general name given to evidential reasoning networks by Shenoy, provides a means to reduce computing requirements through knowledge structure. However, these networks use conditional probabilities or transition potential matrices to describe the relationships between nodes, which typically require expert information to define and update. This paper proposes and tests a novel method to learn these transition potential matrices based on evidence injected at nodes. Novel refinements to the method are also introduced, demonstrating improvements in capturing the relationships between the node belief distributions. Finally, novel rules are introduced and tested for evidence weighting at nodes during simultaneous evidence injections, correctly balancing the injected evidenced used to learn the transition potential matrices. Together, these methods enable updating a Dempster-Shafer network with significantly less user input, thereby making these networks more useful for scenarios in which sufficient information concerning relationships between nodes is not known a priori.

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