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Comparing the linear and logarithm normalized extreme learning machine in flow curve modeling of magnetorheological fluid

The extreme learning machine (ELM) plays an important role to predict magnetorheological (MR) fluid behavior and to reduce the computational fluid dynamics (CFD) calculation cost while simulating the MR fluid flow of an MR actuator. This paper presents a logarithm normalized method to enhance the pr...

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Main Authors: Bahiuddin, I., Fatah, A. Y. A., Mazlan, S. A., Shapiai, M. I., Imaduddin, F., Ubaidillah, Ubaidillah, Utami, D., Muhtazaruddin, M. N.
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2019
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/88878/1/AbdulYasserFatah2019_ComparingtheLinearandLogarithmNormalized.pdf
http://eprints.utm.my/id/eprint/88878/
http://www.dx.doi.org/10.11591/ijeecs.v13.i3.pp1065-1072
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spelling my.utm.888782020-12-29T04:38:56Z http://eprints.utm.my/id/eprint/88878/ Comparing the linear and logarithm normalized extreme learning machine in flow curve modeling of magnetorheological fluid Bahiuddin, I. Fatah, A. Y. A. Mazlan, S. A. Shapiai, M. I. Imaduddin, F. Ubaidillah, Ubaidillah Utami, D. Muhtazaruddin, M. N. T Technology (General) The extreme learning machine (ELM) plays an important role to predict magnetorheological (MR) fluid behavior and to reduce the computational fluid dynamics (CFD) calculation cost while simulating the MR fluid flow of an MR actuator. This paper presents a logarithm normalized method to enhance the prediction of ELM of the flow curve representing the MR fluid rheological properties. MRC C1L was used to test the performance of the proposed method, and different activation functions of ELMs were chosen to be the neural networks setting. The Normalized Root Mean Square Error (NRMSE) was selected as the indicator of the ELM prediction accuracy. NRMSE of the proposed method is found to improve the model accuracy up to 77.10 % for the prediction or testing case while comparing with the linear normalized ELM. Institute of Advanced Engineering and Science 2019-03 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/88878/1/AbdulYasserFatah2019_ComparingtheLinearandLogarithmNormalized.pdf Bahiuddin, I. and Fatah, A. Y. A. and Mazlan, S. A. and Shapiai, M. I. and Imaduddin, F. and Ubaidillah, Ubaidillah and Utami, D. and Muhtazaruddin, M. N. (2019) Comparing the linear and logarithm normalized extreme learning machine in flow curve modeling of magnetorheological fluid. Indonesian Journal of Electrical Engineering and Computer Science, 13 (3). pp. 1065-1072. ISSN 2502-4752 http://www.dx.doi.org/10.11591/ijeecs.v13.i3.pp1065-1072 DOI: 10.11591/ijeecs.v13.i3.pp1065-1072
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Bahiuddin, I.
Fatah, A. Y. A.
Mazlan, S. A.
Shapiai, M. I.
Imaduddin, F.
Ubaidillah, Ubaidillah
Utami, D.
Muhtazaruddin, M. N.
Comparing the linear and logarithm normalized extreme learning machine in flow curve modeling of magnetorheological fluid
description The extreme learning machine (ELM) plays an important role to predict magnetorheological (MR) fluid behavior and to reduce the computational fluid dynamics (CFD) calculation cost while simulating the MR fluid flow of an MR actuator. This paper presents a logarithm normalized method to enhance the prediction of ELM of the flow curve representing the MR fluid rheological properties. MRC C1L was used to test the performance of the proposed method, and different activation functions of ELMs were chosen to be the neural networks setting. The Normalized Root Mean Square Error (NRMSE) was selected as the indicator of the ELM prediction accuracy. NRMSE of the proposed method is found to improve the model accuracy up to 77.10 % for the prediction or testing case while comparing with the linear normalized ELM.
format Article
author Bahiuddin, I.
Fatah, A. Y. A.
Mazlan, S. A.
Shapiai, M. I.
Imaduddin, F.
Ubaidillah, Ubaidillah
Utami, D.
Muhtazaruddin, M. N.
author_facet Bahiuddin, I.
Fatah, A. Y. A.
Mazlan, S. A.
Shapiai, M. I.
Imaduddin, F.
Ubaidillah, Ubaidillah
Utami, D.
Muhtazaruddin, M. N.
author_sort Bahiuddin, I.
title Comparing the linear and logarithm normalized extreme learning machine in flow curve modeling of magnetorheological fluid
title_short Comparing the linear and logarithm normalized extreme learning machine in flow curve modeling of magnetorheological fluid
title_full Comparing the linear and logarithm normalized extreme learning machine in flow curve modeling of magnetorheological fluid
title_fullStr Comparing the linear and logarithm normalized extreme learning machine in flow curve modeling of magnetorheological fluid
title_full_unstemmed Comparing the linear and logarithm normalized extreme learning machine in flow curve modeling of magnetorheological fluid
title_sort comparing the linear and logarithm normalized extreme learning machine in flow curve modeling of magnetorheological fluid
publisher Institute of Advanced Engineering and Science
publishDate 2019
url http://eprints.utm.my/id/eprint/88878/1/AbdulYasserFatah2019_ComparingtheLinearandLogarithmNormalized.pdf
http://eprints.utm.my/id/eprint/88878/
http://www.dx.doi.org/10.11591/ijeecs.v13.i3.pp1065-1072
_version_ 1687393634736930816
score 13.211869

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