Title :
A Method of Multi-sensor Data Fusion Based on Rough Set Theory and ART-2 Neural Network
Author_Institution :
Dept. of Electr. Eng., Shandong Polytech., Jinan, China
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;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on
Print_ISBN :
978-1-4799-7004-9
DOI :
10.1109/ISCID.2014.48
