Title :
Multi-sensor Information Fusion Based on Rough Set Theory
Author :
Lv, Xiu-jiang ; Zhao, Yan ; Yao, Guang-shun ; Lv, Qiao-chu ; Wang, Ning
Author_Institution :
Dept. of Electr. Eng., Changchun Univ. of Technol.
Abstract :
Aiming at the problem that the data in the information fusion often overloads, the method that rough set application in neural network was proposed, in which useful attributes were extracted from given training data and redundant attributes were deleted utilizing numerical analysis ability of rough set theory, so sample size can be reduced. While reducing training time and increasing efficiency, the useful information in the source data set wasn´t lost
Keywords :
neural nets; numerical analysis; rough set theory; sensor fusion; multisensor information fusion; neural network; numerical analysis; rough set theory; training data; Data analysis; Data mining; Electronic mail; Information systems; Neural networks; Numerical analysis; Rough sets; Set theory; Systems engineering and theory; Training data; information fusion; neural network; rough set;
Conference_Titel :
Computational Engineering in Systems Applications, IMACS Multiconference on
Conference_Location :
Beijing
Print_ISBN :
7-302-13922-9
Electronic_ISBN :
7-900718-14-1
DOI :
10.1109/CESA.2006.4281618
