Intelligent learning algorithms for active vibration control

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This data was imported from DBLP:

Authors: Madkour, A., Hossain, M.A., Dahal, K.P. and Yu, H.

Journal: IEEE Trans. Systems, Man, and Cybernetics, Part C

Volume: 37

Pages: 1022-1033

This data was imported from Scopus:

Authors: Madkour, A.A., Hossain, M.A., Dahal, K.P. and Yu, H.

Journal: IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews

Volume: 37

Issue: 5

Pages: 1022-1033

ISSN: 1094-6977

DOI: 10.1109/TSMCC.2007.900640

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. © 2007 IEEE.

This data was imported from Web of Science (Lite):

Authors: Madkour, A., Hossain, M.A., Dahal, K.P. and Yu, H.

Journal: IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS

Volume: 37

Issue: 5

Pages: 1022-1033

eISSN: 1558-2442

ISSN: 1094-6977

DOI: 10.1109/TSMCC.2007.900640

The data on this page was last updated at 04:55 on May 22, 2019.