Modeling magneto-rheological damper using neural network and simulated annealing

This thesis is study about modeling the Magneto-rheological damper using Neural Network and Simulated Annealing method. Five different values of current were used in order to modeling the MR damper, which are 0.0 ampere, 0.5 ampere, 1.0 ampere, 1.5 ampere and 2.0 ampere. In order to modeling the MR...

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Bibliographic Details
Main Author:	Mohamad Luqman, Zaki Monsarif
Format:	Undergraduates Project Papers
Language:	English
Published:	2013
Subjects:	TL Motor vehicles. Aeronautics. Astronautics
Online Access:	http://umpir.ump.edu.my/id/eprint/8267/ http://umpir.ump.edu.my/id/eprint/8267/ http://umpir.ump.edu.my/id/eprint/8267/1/cd8193_68.pdf

id	ump-8267
recordtype	eprints
spelling	ump-82672015-11-16T08:11:37Z http://umpir.ump.edu.my/id/eprint/8267/ Modeling magneto-rheological damper using neural network and simulated annealing Mohamad Luqman, Zaki Monsarif TL Motor vehicles. Aeronautics. Astronautics This thesis is study about modeling the Magneto-rheological damper using Neural Network and Simulated Annealing method. Five different values of current were used in order to modeling the MR damper, which are 0.0 ampere, 0.5 ampere, 1.0 ampere, 1.5 ampere and 2.0 ampere. In order to modeling the MR damper, the graph of simulation damper will be compared with the experimental damper. The results will get the Square Error for the simulation damper. Then, the Root Mean Square Error will be calculated to get the difference between the simulation damper and experimental damper. The results show that the lowest RMSE for the simulation damper were value 1.282457, while the highest RMSE is 13.18909. From the results also, the better current value to modeling the MR damper is using the MR damper with the lowest RMSE. 2013-06 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/8267/1/cd8193_68.pdf Mohamad Luqman, Zaki Monsarif (2013) Modeling magneto-rheological damper using neural network and simulated annealing. Faculty of Mechanical Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:81910&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TL Motor vehicles. Aeronautics. Astronautics
spellingShingle	TL Motor vehicles. Aeronautics. Astronautics Mohamad Luqman, Zaki Monsarif Modeling magneto-rheological damper using neural network and simulated annealing
description	This thesis is study about modeling the Magneto-rheological damper using Neural Network and Simulated Annealing method. Five different values of current were used in order to modeling the MR damper, which are 0.0 ampere, 0.5 ampere, 1.0 ampere, 1.5 ampere and 2.0 ampere. In order to modeling the MR damper, the graph of simulation damper will be compared with the experimental damper. The results will get the Square Error for the simulation damper. Then, the Root Mean Square Error will be calculated to get the difference between the simulation damper and experimental damper. The results show that the lowest RMSE for the simulation damper were value 1.282457, while the highest RMSE is 13.18909. From the results also, the better current value to modeling the MR damper is using the MR damper with the lowest RMSE.
format	Undergraduates Project Papers
author	Mohamad Luqman, Zaki Monsarif
author_facet	Mohamad Luqman, Zaki Monsarif
author_sort	Mohamad Luqman, Zaki Monsarif
title	Modeling magneto-rheological damper using neural network and simulated annealing
title_short	Modeling magneto-rheological damper using neural network and simulated annealing
title_full	Modeling magneto-rheological damper using neural network and simulated annealing
title_fullStr	Modeling magneto-rheological damper using neural network and simulated annealing
title_full_unstemmed	Modeling magneto-rheological damper using neural network and simulated annealing
title_sort	modeling magneto-rheological damper using neural network and simulated annealing
publishDate	2013
url	http://umpir.ump.edu.my/id/eprint/8267/ http://umpir.ump.edu.my/id/eprint/8267/ http://umpir.ump.edu.my/id/eprint/8267/1/cd8193_68.pdf
first_indexed	2023-09-18T22:05:39Z
last_indexed	2023-09-18T22:05:39Z
_version_	1777414690016591872

Modeling magneto-rheological damper using neural network and simulated annealing

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