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Intelligent Learning Algorithms for Active Vibration Control
Madkour, A.A.M. Hossain, M. Alamgir Dahal, Keshav P.
Madkour, A.A.M.
Hossain, M. Alamgir
Dahal, Keshav P.
Publication Date
2007
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© 2007 IEEE. Reprinted from IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews.
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Abstract
This correspondence presents an investigation into the
comparative performance of an active vibration control (AVC) system
using a number of intelligent learning algorithms. Recursive least square
(RLS), evolutionary genetic algorithms (GAs), general regression neural
network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS)
algorithms are proposed to develop the mechanisms of an AVC system.
The controller is designed on the basis of optimal vibration suppression
using a plant model. A simulation platform of a flexible beam system
in transverse vibration using a finite difference method is considered to
demonstrate the capabilities of the AVC system using RLS, GAs, GRNN,
and ANFIS. The simulation model of the AVC system is implemented,
tested, and its performance is assessed for the system identification models
using the proposed algorithms. Finally, a comparative performance of the
algorithms in implementing the model of the AVC system is presented and
discussed through a set of experiments.
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Madkour A, Hossain MA and Dahal KP (2007) Intelligent Learning Algorithms for Active Vibration Control. IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews. 37(5): 1022-1033.
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