Application of data fusion technology based on D-S evidence theory in fire detection

2015 ◽  
Author(s):  
Zhishan Cai ◽  
Musheng Chen
Keyword(s):  
Data Fusion ◽  
Evidence Theory ◽  
Fire Detection ◽  
Fusion Technology ◽  
In Fire

Fault Diagnosis of Analog Circuits Based on Evidence Theory

2014 ◽  
Vol 556-562 ◽  
pp. 2149-2152
Author(s):  
Cheng Cheng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Analog Circuits ◽  
Bp Neural Network ◽  
Analog Circuit ◽  
Evidence Theory ◽  
Fusion Technology ◽  
Diagnosis Method ◽  
Simulation Results

BP neural network and evidence theory data fusion technology can be used in troubleshooting electronic equipment, from the simulation results show that the fault diagnosis method based on evidence theory and BP neural network can effectively diagnose faults in analog circuit, and it has automated intelligent characteristics.

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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