Yes / 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.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/947 |
| Date | January 2007 |
| Creators | Madkour, A.A.M., Hossain, M. Alamgir, Dahal, Keshav P. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article |
| Rights | © 2007 IEEE. Reprinted from IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bradford's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. |
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