Three algorithms for learning artificial neural network: A comparison for induction motor flux estimation

Rafiq, M. Abdur, Roy, Naruttam Kumar and Ghosh, B.C. 2009, Three algorithms for learning artificial neural network: A comparison for induction motor flux estimation, in ICCIT 2009 : Proceedings of the 12th International Conference on Computers and Information Technology, IEEE, Piscataway, N.J., pp. 355-360, doi: 10.1109/ICCIT.2009.5407263.

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Title Three algorithms for learning artificial neural network: A comparison for induction motor flux estimation
Author(s) Rafiq, M. Abdur
Roy, Naruttam Kumar
Ghosh, B.C.
Conference name Computers and Information Technology. Conference (12th : 2009 : Dhaka, Bangladesh)
Conference location Dhaka, Bangladesh
Conference dates 21-23 Dec. 2009
Title of proceedings ICCIT 2009 : Proceedings of the 12th International Conference on Computers and Information Technology
Editor(s) [Unknown]
Publication date 2009
Conference series Computers and Information Technology Conference
Start page 355
End page 360
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Summary This paper presents a comparative study of three algorithms for learning artificial neural network. As neural estimator, back-propagation (BP) algorithm, uncorrelated real time recurrent learning (URTRL) algorithm and correlated real time recurrent learning (CRTRL) algorithm are used in the present work to learn the artificial neural network (ANN). The approach proposed here is based on the flux estimation of high performance induction motor drives. Simulation of the drive system was carried out to study the performance of the motor drive. It is observed that the proposed CRTRL algorithm based methodology provides better performance than the BP and URTRL algorithm based technique. The proposed method can be used for accurate measurement of the rotor flux.
ISBN 9781424462810
Language eng
DOI 10.1109/ICCIT.2009.5407263
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30064194

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