Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

The Application of Tolerant Rough Set Neural Network to Fighter Fault Diagnosis

Authors
Guoqiang Sun, Hongli Wang, Jun Tao, Xubing Li
Corresponding Author
Guoqiang Sun
Available Online March 2013.
DOI
https://doi.org/10.2991/iccsee.2013.763How to use a DOI?
Keywords
Rough Set, Tolerant Relation, Fault Diagnosis, Neural Network
Abstract
Conventional rough set theory is based on indiscernibility relation, which lacks the adaptive ability to data noise or data missing. Furthermore, it may present qualitatively whether or not the faults exist, but it can’t compute accurately the value of the faults. Though the neural network has ability of approximating unknown nonlinear systems, but it can’t distinguish the redundant knowledge from useful knowledge, so it’s classification ability can’t catch up with the rough set classifier. This paper combines the rough set theory and the tolerant rough set neural network to diagnose the rudder faults of fighter, which solves well the problem of fault diagnosis and fault degree computation. Simulation results demonstrate the effectiveness of the proposed method.
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Proceedings
Conference of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
Part of series
Advances in Intelligent Systems Research
Publication Date
March 2013
ISBN
978-90-78677-61-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/iccsee.2013.763How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Guoqiang Sun
AU  - Hongli Wang
AU  - Jun Tao
AU  - Xubing Li
PY  - 2013/03
DA  - 2013/03
TI  - The Application of Tolerant Rough Set Neural Network to Fighter Fault Diagnosis
BT  - Conference of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 3057
EP  - 3060
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.763
DO  - https://doi.org/10.2991/iccsee.2013.763
ID  - Sun2013/03
ER  -