An Improved Multi-Source Data Fusion Method Based on the Belief Entropy and Divergence Measure
AbstractDempster–Shafer (DS) evidence theory is widely applied in multi-source data fusion technology. However, classical DS combination rule fails to deal with the situation when evidence is highly in conflict. To address this problem, a novel multi-source data fusion method is proposed in this paper. The main steps of the proposed method are presented as follows. Firstly, the credibility weight of each piece of evidence is obtained after transforming the belief Jenson–Shannon divergence into belief similarities. Next, the belief entropy of each piece of evidence is calculated and the information volume weights of evidence are generated. Then, both credibility weights and information volume weights of evidence are unified to generate the final weight of each piece of evidence before the weighted average evidence is calculated. Then, the classical DS combination rule is used multiple times on the modified evidence to generate the fusing results. A numerical example compares the fusing result of the proposed method with that of other existing combination rules. Further, a practical application of fault diagnosis is presented to illustrate the plausibility and efficiency of the proposed method. The experimental result shows that the targeted type of fault is recognized most accurately by the proposed method in comparing with other combination rules. View Full-Text
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Wang, Z.; Xiao, F. An Improved Multi-Source Data Fusion Method Based on the Belief Entropy and Divergence Measure. Entropy 2019, 21, 611.
Wang Z, Xiao F. An Improved Multi-Source Data Fusion Method Based on the Belief Entropy and Divergence Measure. Entropy. 2019; 21(6):611.Chicago/Turabian Style
Wang, Zhe; Xiao, Fuyuan. 2019. "An Improved Multi-Source Data Fusion Method Based on the Belief Entropy and Divergence Measure." Entropy 21, no. 6: 611.
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