DocumentCode
3579825
Title
A Method of Multi-sensor Data Fusion Based on Rough Set Theory and ART-2 Neural Network
Author
Yongjun Zhao
Author_Institution
Dept. of Electr. Eng., Shandong Polytech., Jinan, China
Volume
1
fYear
2014
Firstpage
238
Lastpage
240
Abstract
ART-2 is a self-organized and unsupervised artificial neural network which can be used to deal with data fusion. But we found that the problem of data overloading is hard to be solved in the process of data fusion. Rough set is a mathematical approach to deal with vague, uncertain and imperfect data. So, we proposed a method of multi-sensor data fusion based on rough set theory and ART-2 neural network. At last, the simulation results show the feasibility and the validity of the proposed fusion system.
Keywords
ART neural nets; rough set theory; sensor fusion; ART-2 neural network; data overloading; imperfect data; multisensor data fusion; rough set theory; self-organized artificial neural network; uncertain data; unsupervised artificial neural network; vague data; Approximation methods; Biological neural networks; Data integration; Neurons; Set theory; ART-2 Neural Network; Multi-sensor Data Fusion; Rough Set Theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on
Print_ISBN
978-1-4799-7004-9
Type
conf
DOI
10.1109/ISCID.2014.48
Filename
7064181
Link To Document