Ant colony based model prediction of a twin rotor system
Interest in biologically-inspired optimization techniques has increased due to its accurate results, fast performance and ease of use. In this paper, an ant colony optimization (ACO) technique is deployed and used for modelling a twin rotor system. The system is perceived as a challenging engineerin...
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| Language: | English |
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Elsevier
2012
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| Online Access: | http://irep.iium.edu.my/38671/ http://irep.iium.edu.my/38671/ http://irep.iium.edu.my/38671/ http://irep.iium.edu.my/38671/1/ProcediaEngineering_ACO.pdf |
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iium-386712014-10-09T08:16:57Z http://irep.iium.edu.my/38671/ Ant colony based model prediction of a twin rotor system Toha, Siti Fauziah Julai, S. Tokhi, M. Osman TL500 Aeronautics Interest in biologically-inspired optimization techniques has increased due to its accurate results, fast performance and ease of use. In this paper, an ant colony optimization (ACO) technique is deployed and used for modelling a twin rotor system. The system is perceived as a challenging engineering problem due to its strong cross coupling between horizontal and vertical axes and inaccessibility of some of its states and outputs for measurements. Accurate modelling of the system is thus required so as to achieve satisfactory control objectives. It is demonstrated that ACO can be effectively used for modelling the system with highly accurate results. The accuracy of the modelling results is demonstrated through validation tests including training and test validation and correlation tests. Elsevier 2012 Article PeerReviewed application/pdf en http://irep.iium.edu.my/38671/1/ProcediaEngineering_ACO.pdf Toha, Siti Fauziah and Julai, S. and Tokhi, M. Osman (2012) Ant colony based model prediction of a twin rotor system. Procedia Engineering, 41. pp. 1135-1144. ISSN 1877-7058 http://www.sciencedirect.com/science/article/pii/S1877705812026938 10.1016/j.proeng.2012.07.293 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
TL500 Aeronautics |
| spellingShingle |
TL500 Aeronautics Toha, Siti Fauziah Julai, S. Tokhi, M. Osman Ant colony based model prediction of a twin rotor system |
| description |
Interest in biologically-inspired optimization techniques has increased due to its accurate results, fast performance and ease of use. In this paper, an ant colony optimization (ACO) technique is deployed and used for modelling a twin rotor system. The system is perceived as a challenging engineering problem due to its strong cross coupling between horizontal and vertical axes and inaccessibility of some of its states and outputs for measurements. Accurate modelling of the system is thus required so as to achieve satisfactory control objectives. It is demonstrated that ACO can be effectively used for modelling the system with highly accurate results. The accuracy of the modelling results is demonstrated through validation tests including training and test validation and correlation tests. |
| format |
Article |
| author |
Toha, Siti Fauziah Julai, S. Tokhi, M. Osman |
| author_facet |
Toha, Siti Fauziah Julai, S. Tokhi, M. Osman |
| author_sort |
Toha, Siti Fauziah |
| title |
Ant colony based model prediction of a twin rotor system |
| title_short |
Ant colony based model prediction of a twin rotor system |
| title_full |
Ant colony based model prediction of a twin rotor system |
| title_fullStr |
Ant colony based model prediction of a twin rotor system |
| title_full_unstemmed |
Ant colony based model prediction of a twin rotor system |
| title_sort |
ant colony based model prediction of a twin rotor system |
| publisher |
Elsevier |
| publishDate |
2012 |
| url |
http://irep.iium.edu.my/38671/ http://irep.iium.edu.my/38671/ http://irep.iium.edu.my/38671/ http://irep.iium.edu.my/38671/1/ProcediaEngineering_ACO.pdf |
| first_indexed |
2023-09-18T20:55:34Z |
| last_indexed |
2023-09-18T20:55:34Z |
| _version_ |
1777410280771289088 |