DocumentCode :
2755641
Title :
Novel Hybrid Approach for Fault Diagnosis in 3-DOF Flight Simulator Based on BP Neural Network and Ant Colony Algorithm
Author :
Duan, Haibin ; Yu, Xiufen ; Ma, Guanjun
Author_Institution :
Sch. of Autom. Sci. & Electr. Eng., Beihang Univ., Beijing
fYear :
2007
fDate :
1-5 April 2007
Firstpage :
371
Lastpage :
374
Abstract :
In the 3-DOF(degree-of-freedom) flight simulator system, the relations between observed information and fault causes are very complicated. Based on the description of the basic principle of the ant colony algorithm, a novel hybrid approach for fault diagnosis in 3-DOF flight simulator is proposed in this paper, which is based on BP(back propagation) neural network and ant colony algorithm. Combining with rough set theory, ant colony algorithm is used to compute the reductions of the decision table. Then, the condition attributes of decision table are regarded as the input nodes of BP neural network and the decision attributes are regarded as the output nodes of BP neural network correspondingly. Experiments demonstrate that the proposed hybrid approach could achieve a fairly good performance, yield good prediction accuracy of the prediction errors
Keywords :
aerospace computing; aerospace simulation; backpropagation; decision tables; fault diagnosis; neural nets; optimisation; rough set theory; 3-degree-of-freedom flight simulator; ant colony algorithm; back propagation; decision table reduction; fault diagnosis; neural network; rough set theory; Accuracy; Aerospace simulation; Ant colony optimization; Computational modeling; Fault diagnosis; Instruments; Neural networks; Particle swarm optimization; Servomechanisms; Set theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Swarm Intelligence Symposium, 2007. SIS 2007. IEEE
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0708-7
Type :
conf
DOI :
10.1109/SIS.2007.367962
Filename :
4223199
Link To Document :
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