The Application of Tolerant Rough Set Neural Network to Fighter Fault Diagnosis
Guoqiang Sun, Hongli Wang, Jun Tao, Xubing Li
Available Online March 2013.
- https://doi.org/10.2991/iccsee.2013.763How to use a DOI?
- Rough Set, Tolerant Relation, Fault Diagnosis, Neural Network
- 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.
- 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 -