This paper gives a data fusion structure based on RBF neural network and D-S inference and its application in the fault diagnosis of bearing.
提出一种基于RBF神经网络和D - S证据理论相结合的数据融合结构应用于轴承故障诊断。
Considering the concrete instance of the OE tracking and measuring system, it takes the weighted average method as the data fusion method after analyzing the data fusion structure.
从光电跟踪系统的具体情况出发，经过对数据融合结构的分析，确定融合方法为加权平均的融合方法。
Then the advantages of existing data fusion models are synthesized and a kind of hybrid layered fusion structure is used to improve the accuracy and effectiveness of autonomous driving.
然后综合现有各数据融合模型的优势，运用混合、分层的融合结构改进自主行驶的有效性和准确性。
应用推荐