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

Author

Gao, Wei ; Wen, Jingxin ; Jiang, Nan ; Zhao, Hai

Author_Institution

Inst. of Inf. & Technol., Northeastern Univ., Shenyang, China

Volume

3

fYear

2009

fDate

12-14 Aug. 2009

Firstpage

103

Lastpage

106

Abstract

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.

Keywords

backpropagation; channel capacity; data mining; neural nets; rough set theory; sensor fusion; BP neural network; attribute reduction; channel capacity; data fusion; identification rate; knowledge relative reduction algorithm; redundant information elimination; rough set theory; Artificial intelligence; Artificial neural networks; Channel capacity; Chemical technology; Feedforward neural networks; Feedforward systems; Neural networks; Neurons; Set theory; Space technology; BP algorithm; attribute reduction; data fusion; neural network; rough set;

fLanguage

English

Publisher

ieee

Conference_Titel

Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference on

Conference_Location

Shenyang

Print_ISBN

978-0-7695-3745-0

Type

conf

DOI

10.1109/HIS.2009.233

Filename

5254543