A Method of Data Fusion System for Fault Detection Based on Model-based Diagnosis and Evidence Theory

2008 ◽  
Author(s):  
Yao Qin ◽  
Shi Yi-kai ◽  
Shan Ning
Keyword(s):  
Fault Detection ◽  
Data Fusion ◽  
Evidence Theory ◽  
Fusion System ◽  
Model Based

Target Classification Using PAS and Evidence Theory

2013 ◽  
Vol 347-350 ◽  
pp. 3728-3733
Author(s):  
Li Jia ◽  
Hai Yan ◽  
Guo Hui Li ◽  
Hui Zhang
Keyword(s):  
Data Fusion ◽  
Prior Probability ◽  
Evidence Theory ◽  
Real Data ◽  
Fusion System ◽  
Target Classification ◽  
Additional Information ◽  
Argumentation System ◽  
Probabilistic Argumentation

This paper presents a novel Dempster-Shafer evidence construction approach for aircraft aim recognition. The prior-probability of the properties of aircraft was used for establishing a probabilistic argumentation system. Dempster-Shafer evidence was constructed by assumption-based reasoning. Therefore, additional information could be provided to the classification of the data fusion system. Experiments on artificial and real data demonstrated that the proposed method could improve the classification results.

scholarly journals Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 820
Author(s):  
Jingyu Liu ◽  
Yongchuan Tang
Keyword(s):  
Data Fusion ◽  
Evidence Theory ◽  
Multi Agent System ◽  
Agent System ◽  
Combination Rules ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Reasonable Evidence ◽  
Basic Probability ◽  
Multi Agent

The multi-agent information fusion (MAIF) system can alleviate the limitations of a single expert system in dealing with complex situations, as it allows multiple agents to cooperate in order to solve problems in complex environments. Dempster–Shafer (D-S) evidence theory has important applications in multi-source data fusion, pattern recognition, and other fields. However, the traditional Dempster combination rules may produce counterintuitive results when dealing with highly conflicting data. A conflict data fusion method in a multi-agent system based on the base basic probability assignment (bBPA) and evidence distance is proposed in this paper. Firstly, the new bBPA and reconstructed BPA are used to construct the initial belief degree of each agent. Then, the information volume of each evidence group is obtained by calculating the evidence distance so as to modify the reliability and obtain more reasonable evidence. Lastly, the final evidence is fused with the Dempster combination rule to obtain the result. Numerical examples show the effectiveness and availability of the proposed method, which improves the accuracy of the identification process of the MAIF system.

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