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首页> 外文期刊>Zeitschrift fur Arznei- und Gewurzpflanzen >Data Fusion Algorithm Based on Fuzzy Sets and D-S Theory of Evidence
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Data Fusion Algorithm Based on Fuzzy Sets and D-S Theory of Evidence

机译:基于模糊套和D-S证据理论的数据融合算法

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摘要

In cyber-physical systems, multidimensional data fusion is an important method to achieve comprehensive evaluation decisions and reduce data redundancy. In this paper, a data fusion algorithm based on fuzzy set theory and Dempster-Shafer (D-S) evidence theory is proposed to overcome the shortcomings of the existing decision-layer multidimensional data fusion algorithms. The basic probability distribution of evidence is determined based on fuzzy set theory and attribute weights, and the data fusion of attribute evidence is combined with the credibility of sensor nodes in a cyber-physical systems network. Experimental analysis shows that the proposed method has obvious advantages in the degree of the differentiation of the results.
机译:在网络物理系统中,多维数据融合是实现全面评估决策并降低数据冗余的重要方法。 本文采用了基于模糊集理论和Dempster-Shafer(D-S)证据理论的数据融合算法,以克服现有决策层多维数据融合算法的缺点。 证据的基本概率分布是基于模糊集理论和属性权重确定的,并且属性证据的数据融合与网络物理系统网络中的传感器节点的可信度相结合。 实验分析表明,该方法在结果的分化程度中具有明显的优势。

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