Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization
This paper present parameter identification fitting which are employed into a current model. Irregularity hysteresis of Bouc-Wen model is colloquial with magneto-rheological (MR) fluid damper. The model parameters are identified with a Particle Swarm Optimization (PSO) which involves complex dynamic...
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Trans Tech Publications, Switzerland
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oai:umpir.ump.edu.my:55322018-04-19T02:51:52Z http://umpir.ump.edu.my/id/eprint/5532/ Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization Mohd Azraai, M. Razman Priyandoko, Gigih A. R., Yusoff TS Manufactures This paper present parameter identification fitting which are employed into a current model. Irregularity hysteresis of Bouc-Wen model is colloquial with magneto-rheological (MR) fluid damper. The model parameters are identified with a Particle Swarm Optimization (PSO) which involves complex dynamic representation. The PSO algorithm specifically determines the best fit value and decrease marginal error which compare to the experimental data from various operating conditions in a given boundary. Trans Tech Publications, Switzerland 2014 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5532/1/12.pdf Mohd Azraai, M. Razman and Priyandoko, Gigih and A. R., Yusoff (2014) Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization. Advanced Materials Research, 903. pp. 279-284. ISSN 1022-6680 (print), 1662-8985 (online) http://dx.doi.org/10.4028/www.scientific.net/AMR.903.279 DOI: 10.4028/www.scientific.net/AMR.903.279 |
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TS Manufactures Mohd Azraai, M. Razman Priyandoko, Gigih A. R., Yusoff Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization |
description |
This paper present parameter identification fitting which are employed into a current model. Irregularity hysteresis of Bouc-Wen model is colloquial with magneto-rheological (MR) fluid damper. The model parameters are identified with a Particle Swarm Optimization (PSO) which involves complex dynamic representation. The PSO algorithm specifically determines the best fit value and decrease marginal error which compare to the experimental data from various operating conditions in a given boundary. |
format |
Article |
author |
Mohd Azraai, M. Razman Priyandoko, Gigih A. R., Yusoff |
author_facet |
Mohd Azraai, M. Razman Priyandoko, Gigih A. R., Yusoff |
author_sort |
Mohd Azraai, M. Razman |
title |
Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization |
title_short |
Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization |
title_full |
Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization |
title_fullStr |
Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization |
title_full_unstemmed |
Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization |
title_sort |
bouc-wen model parameter identification for a mr fluid damper using particle swarm optimization |
publisher |
Trans Tech Publications, Switzerland |
publishDate |
2014 |
url |
http://umpir.ump.edu.my/id/eprint/5532/ http://umpir.ump.edu.my/id/eprint/5532/ http://umpir.ump.edu.my/id/eprint/5532/ http://umpir.ump.edu.my/id/eprint/5532/1/12.pdf |
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2018-09-07T00:47:20Z |
last_indexed |
2018-09-07T00:47:20Z |
_version_ |
1610907638663479296 |