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An Improved Multi-Source Data Fusion Method Based on the Belief Entropy and Divergence Measure.
1School of Computer and Information Science, Southwest University, No.2 Tiansheng Road, BeiBei District, Chongqing 400715, China.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
This study introduces a new Dempster-Shafer (DS) evidence theory method for multi-source data fusion. The novel approach improves fault diagnosis accuracy by effectively handling conflicting evidence.
Area of Science:
- Artificial Intelligence
- Information Fusion
- Decision Support Systems
Background:
- Dempster-Shafer (DS) evidence theory is a cornerstone of multi-source data fusion.
- Classical DS combination rules struggle with highly conflicting evidence, limiting their practical application.
- Effective fusion of uncertain and conflicting information is crucial for robust decision-making.
Purpose of the Study:
- To propose a novel multi-source data fusion method based on DS evidence theory.
- To address the limitations of classical DS combination rules in handling conflicting evidence.
- To enhance the accuracy and efficiency of data fusion, particularly in fault diagnosis applications.
Main Methods:
- A new method is proposed that assigns credibility and information volume weights to evidence.
- Evidence credibility is determined by transforming Jenson-Shannon divergence into belief similarities.
- Unified weights are used for a weighted average, followed by iterative application of the classical DS combination rule.
Main Results:
- The proposed method demonstrates superior performance in fusing conflicting evidence compared to existing rules.
- Numerical examples validate the effectiveness of the novel fusion approach.
- A fault diagnosis application shows the proposed method achieves the highest accuracy in identifying fault types.
Conclusions:
- The novel DS evidence theory-based fusion method effectively handles conflicting evidence.
- The proposed method offers improved accuracy and efficiency for multi-source data fusion.
- This approach shows significant promise for practical applications like fault diagnosis.
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