TY - GEN
T1 - Neural network integration fusion model and application
AU - Zhang, Xiaodan
AU - Niu, Zhendong
PY - 2008
Y1 - 2008
N2 - A new fusion model is proposed, which is the combination of BP neural networks and Rough Set algorithm, to solve the problems of low precision rate in Aircraft engine fault diagnosis by traditional methods. The method realizes feature level fusion of all subjective data and expert experiments on different parts of engine, and the predominance compensation of different models. In simulation experiment, the method proposed in the paper can improve diagnosis precision 5.0% more than expert system.
AB - A new fusion model is proposed, which is the combination of BP neural networks and Rough Set algorithm, to solve the problems of low precision rate in Aircraft engine fault diagnosis by traditional methods. The method realizes feature level fusion of all subjective data and expert experiments on different parts of engine, and the predominance compensation of different models. In simulation experiment, the method proposed in the paper can improve diagnosis precision 5.0% more than expert system.
UR - http://www.scopus.com/inward/record.url?scp=67449147156&partnerID=8YFLogxK
U2 - 10.1109/ISDA.2008.331
DO - 10.1109/ISDA.2008.331
M3 - Conference contribution
AN - SCOPUS:67449147156
SN - 9780769533827
T3 - Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008
SP - 213
EP - 215
BT - Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008
T2 - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008
Y2 - 26 November 2008 through 28 November 2008
ER -