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

摘要：

Based on rough set and basic theory of data fusion, the data fusion algorithm combining rough set theory and BP neural network is studied. Since rough set theory can effectively simplify information, cut down the tagged dimension . This paper will be rough set theory and neural networks combined, using channel capacity of knowledge relative reduction algorithms to simplify the input information. 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 improved algorithm is demonstrated by an example compared with the traditional neural network system.

关键词： rough set neural network BP algorithm data fusion attribute reduction

作者: Wei Gao Jingxin Wen Nan Jiang Hai Zhao

作者单位: Institute of Information and Technology Northeastern University Shenyang Institute of Chemical Techn Shenyang Institute of Chemical Technology Shenyang, China Institute of Information and Technology Northeastern University Shenyang, China

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-4

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）