Modelling magneto-rheological damper using radial basis function neural network / Mohd Fikri Arifin

By: Material type: TextTextPublication details: Kuantan, Pahang : UMP, 2013Description: xvii, 48 p. : ill. ; 30 cm. + 1 CD-ROMISBN:
  • THE0007280(Local)
Subject(s): Dissertation note: Project paper (Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang - 2013 Abstract: This 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
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Final Year Report Final Year Report UMPLIB PEKAN TL257 .F55 2013 r Bc. (Browse shelf(Opens below)) 1 Not for loan 0000086174
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Project paper (Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang - 2013

Bibliography : p. 48

This 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

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