Wang Zhe, Xiao Fuyuan
School of Computer and Information Science, Southwest University, No.2 Tiansheng Road, BeiBei District, Chongqing 400715, China.
Entropy (Basel). 2019 Jun 20;21(6):611. doi: 10.3390/e21060611.
Dempster-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.
邓普斯特 - 谢弗（DS）证据理论在多源数据融合技术中得到了广泛应用。然而，经典的DS组合规则在证据高度冲突的情况下无法处理。为了解决这个问题，本文提出了一种新颖的多源数据融合方法。该方法的主要步骤如下。首先，将置信詹森 - 香农散度转化为置信相似度后，得到每条证据的可信度权重。接下来，计算每条证据的置信熵并生成证据的信息量权重。然后，将证据的可信度权重和信息量权重统一起来，在计算加权平均证据之前生成每条证据的最终权重。接着，对修正后的证据多次使用经典的DS组合规则以生成融合结果。通过一个数值例子将所提方法的融合结果与其他现有组合规则的融合结果进行比较。此外，还给出了故障诊断的实际应用以说明所提方法的合理性和有效性。实验结果表明，与其他组合规则相比，所提方法能最准确地识别出目标故障类型。
