粗糙集结合BP神经网络的数据融合方法研究

A Study of Data Fusion Based on Combining Rough Set with BP Neural Network

在粗糙集和数据融合基本理论的基础上,研究了基于粗糙集理论和BP神经网络相结合的数据融合方法。先利用粗糙集对输入信息进行简化,剔除冗余信息,从而缩减了BP神经网络的规模,提高了融合系统的识别率,进而提高整个融合系统的效率。与传统的神经网络融合系统进行比较,通过实例说明了该方法的有效性。

Based on rough set and basic theory of data fusion, the data fusion algorithm combining rough set theory and BP neural network is studied. Rough set theory is first used to process the sample data,and eliminate the redundant information, then reduce the scale of neural network, improve the identification rate, and improve the efficiency of the whole data fusion system. The effectiveness of the algorithm is demonstrated by an example compared with the traditional neural network system.