NDLI: Novel Hybrid Approach for Fault Diagnosis in 3-DOF Flight Simulator Based on BP Neural Network and Ant Colony Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Haibin Duan Xiufen Yu Guanjun Ma
Copyright Year	2007
Description	Author affiliation: Sch. of Autom. Sci. & Electr. Eng., Beihang Univ., Beijing (Haibin Duan)
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
Starting Page	371
Ending Page	374
File Size	4374380
Page Count	4
File Format	PDF
ISBN	1424407087
DOI	10.1109/SIS.2007.367962
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Ant colony optimization Accuracy Computational modeling Instruments Neural networks Aerospace simulation Set theory Servomechanisms Particle swarm optimization
Content Type	Text
Resource Type	Article

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