Modelling magneto-rheological damper using radial basis function neural network
This is a study on modeling the MR damper using RBF. MR damper can be simplify as a damper that using MR fluids. MR fluid contains magnetic particles which will react to current flow when power is supplied. The viscosity of the fluid depends on the current flow. The stiffness of the damper depends o...
| Main Author: | |
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/8381/1/CD8028_%40_48.pdf |
| _version_ | 1796990431027789824 |
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| author | Mohd Fikri, Arifin |
| author_facet | Mohd Fikri, Arifin |
| author_sort | Mohd Fikri, Arifin |
| collection | UMP |
| description | This is a study on modeling the MR damper using RBF. MR damper can be simplify as a damper that using MR fluids. MR fluid contains magnetic particles which will react to current flow when power is supplied. The viscosity of the fluid depends on the current flow. The stiffness of the damper depends on the fluids. This modeling is to achieve the similarity of the results of the experiment using proper machine and apparatus and by using MATLAB software. The data that are obtained from the experiment are used in the MATLAB software to generate graphs. The RBF equations are used in the m-file to get the similarity as the graph from experiment. Comparisons between the graphs are decided by inspection and the most accurate, by using the RMSE graph. The input in m-file is adjusted again and again to get the smallest RMSE as possible |
| first_indexed | 2024-03-06T11:51:27Z |
| format | Undergraduates Project Papers |
| id | UMPir8381 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T11:51:27Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | UMPir83812021-07-08T04:17:30Z http://umpir.ump.edu.my/id/eprint/8381/ Modelling magneto-rheological damper using radial basis function neural network Mohd Fikri, Arifin TL Motor vehicles. Aeronautics. Astronautics This is a study on modeling the MR damper using RBF. MR damper can be simplify as a damper that using MR fluids. MR fluid contains magnetic particles which will react to current flow when power is supplied. The viscosity of the fluid depends on the current flow. The stiffness of the damper depends on the fluids. This modeling is to achieve the similarity of the results of the experiment using proper machine and apparatus and by using MATLAB software. The data that are obtained from the experiment are used in the MATLAB software to generate graphs. The RBF equations are used in the m-file to get the similarity as the graph from experiment. Comparisons between the graphs are decided by inspection and the most accurate, by using the RMSE graph. The input in m-file is adjusted again and again to get the smallest RMSE as possible 2013-06 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/8381/1/CD8028_%40_48.pdf Mohd Fikri, Arifin (2013) Modelling magneto-rheological damper using radial basis function neural network. Faculty of Mechanical Engineering , Universiti Malaysia Pahang. |
| spellingShingle | TL Motor vehicles. Aeronautics. Astronautics Mohd Fikri, Arifin Modelling magneto-rheological damper using radial basis function neural network |
| title | Modelling magneto-rheological damper using radial basis function neural network |
| title_full | Modelling magneto-rheological damper using radial basis function neural network |
| title_fullStr | Modelling magneto-rheological damper using radial basis function neural network |
| title_full_unstemmed | Modelling magneto-rheological damper using radial basis function neural network |
| title_short | Modelling magneto-rheological damper using radial basis function neural network |
| title_sort | modelling magneto rheological damper using radial basis function neural network |
| topic | TL Motor vehicles. Aeronautics. Astronautics |
| url | http://umpir.ump.edu.my/id/eprint/8381/1/CD8028_%40_48.pdf |
| work_keys_str_mv | AT mohdfikriarifin modellingmagnetorheologicaldamperusingradialbasisfunctionneuralnetwork |