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2009 Ninth International Conference on Hybrid Intelligent Systems
A Study of Data Fusion Based on Combining Rough Set with BP Neural Network
Shenyang, China
August 12-August 14
ISBN: 978-0-7695-3745-0
| ASCII Text | x | ||
| Wei Gao, Jingxin Wen, Nan Jiang, Hai Zhao, "A Study of Data Fusion Based on Combining Rough Set with BP Neural Network," Hybrid Intelligent Systems, International Conference on, vol. 3, pp. 103-106, 2009 Ninth International Conference on Hybrid Intelligent Systems, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/HIS.2009.233, author = {Wei Gao and Jingxin Wen and Nan Jiang and Hai Zhao}, title = {A Study of Data Fusion Based on Combining Rough Set with BP Neural Network}, journal ={Hybrid Intelligent Systems, International Conference on}, volume = {3}, year = {2009}, isbn = {978-0-7695-3745-0}, pages = {103-106}, doi = {http://doi.ieeecomputersociety.org/10.1109/HIS.2009.233}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Hybrid Intelligent Systems, International Conference on TI - A Study of Data Fusion Based on Combining Rough Set with BP Neural Network SN - 978-0-7695-3745-0 SP103 EP106 A1 - Wei Gao, A1 - Jingxin Wen, A1 - Nan Jiang, A1 - Hai Zhao, PY - 2009 KW - rough set KW - neural network KW - BP algorithm KW - data fusion KW - attribute reduction VL - 3 JA - Hybrid Intelligent Systems, International Conference on ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/HIS.2009.233
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.
Index Terms:
rough set, neural network, BP algorithm, data fusion, attribute reduction
Citation:
Wei Gao, Jingxin Wen, Nan Jiang, Hai Zhao, "A Study of Data Fusion Based on Combining Rough Set with BP Neural Network," his, vol. 3, pp.103-106, 2009 Ninth International Conference on Hybrid Intelligent Systems, 2009
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