Novel hybrid approach for fault diagnosis in 3-DOF flight Simulator based on BP neural network and ant colony algorithm

Haibin Duan; Xiufen Yu; Ma Guanjun

doi:10.1109/SIS.2007.367962

Novel hybrid approach for fault diagnosis in 3-DOF flight Simulator based on BP neural network and ant colony algorithm

Haibin Duan^*
, Xiufen Yu
, Ma Guanjun

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

IEEE
CAS - National Space Science Center
Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	Proceedings of the 2007 IEEE Swarm Intelligence Symposium, SIS 2007
Publisher	IEEE Computer Society
Pages	371-375
Number of pages	5
ISBN (Print)	1424407087, 9781424407088
DOIs	https://doi.org/10.1109/SIS.2007.367962
State	Published - 2007
Event	2007 IEEE Swarm Intelligence Symposium, SIS 2007, Part of the 2007 IEEE Symposium Series on Computational Intelligence, SSCI 2007 - Honolulu, HI, United States Duration: 1 Apr 2007 → 5 Apr 2007

Publication series

Name	Proceedings of the 2007 IEEE Swarm Intelligence Symposium, SIS 2007

Conference

Conference	2007 IEEE Swarm Intelligence Symposium, SIS 2007, Part of the 2007 IEEE Symposium Series on Computational Intelligence, SSCI 2007
Country/Territory	United States
City	Honolulu, HI
Period	1/04/07 → 5/04/07

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10.1109/SIS.2007.367962

Cite this

Duan, H., Yu, X., & Guanjun, M. (2007). Novel hybrid approach for fault diagnosis in 3-DOF flight Simulator based on BP neural network and ant colony algorithm. In Proceedings of the 2007 IEEE Swarm Intelligence Symposium, SIS 2007 (pp. 371-375). Article 4223199 (Proceedings of the 2007 IEEE Swarm Intelligence Symposium, SIS 2007). IEEE Computer Society. https://doi.org/10.1109/SIS.2007.367962

@inproceedings{5900d07b98044a48b6a20c8bc467c0c2,

title = "Novel hybrid approach for fault diagnosis in 3-DOF flight Simulator based on BP neural network and ant colony algorithm",

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.",

author = "Haibin Duan and Xiufen Yu and Ma Guanjun",

year = "2007",

doi = "10.1109/SIS.2007.367962",

language = "英语",

isbn = "1424407087",

series = "Proceedings of the 2007 IEEE Swarm Intelligence Symposium, SIS 2007",

publisher = "IEEE Computer Society",

pages = "371--375",

booktitle = "Proceedings of the 2007 IEEE Swarm Intelligence Symposium, SIS 2007",

address = "美国",

note = "2007 IEEE Swarm Intelligence Symposium, SIS 2007, Part of the 2007 IEEE Symposium Series on Computational Intelligence, SSCI 2007 ; Conference date: 01-04-2007 Through 05-04-2007",

}

Duan, H, Yu, X & Guanjun, M 2007, Novel hybrid approach for fault diagnosis in 3-DOF flight Simulator based on BP neural network and ant colony algorithm. in Proceedings of the 2007 IEEE Swarm Intelligence Symposium, SIS 2007., 4223199, Proceedings of the 2007 IEEE Swarm Intelligence Symposium, SIS 2007, IEEE Computer Society, pp. 371-375, 2007 IEEE Swarm Intelligence Symposium, SIS 2007, Part of the 2007 IEEE Symposium Series on Computational Intelligence, SSCI 2007, Honolulu, HI, United States, 1/04/07. https://doi.org/10.1109/SIS.2007.367962

Novel hybrid approach for fault diagnosis in 3-DOF flight Simulator based on BP neural network and ant colony algorithm. / Duan, Haibin; Yu, Xiufen; Guanjun, Ma.
Proceedings of the 2007 IEEE Swarm Intelligence Symposium, SIS 2007. IEEE Computer Society, 2007. p. 371-375 4223199 (Proceedings of the 2007 IEEE Swarm Intelligence Symposium, SIS 2007).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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Y2 - 1 April 2007 through 5 April 2007

ER -

Novel hybrid approach for fault diagnosis in 3-DOF flight Simulator based on BP neural network and ant colony algorithm

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