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008 140425t2012 my a f m 000 0 eng d
020 _aTHE0007376(Local)
039 9 _a201905151117
_bfawwaz
_c201905151117
_dfawwaz
_c201905151053
_dfawwaz
_c201404291000
_dFida
_y201404251548
_ztraining4
040 _aUMP
090 _aTL574.S7 F37 2012 rs Bc.
100 0 _aFarah Wahida Yusof
245 1 0 _aModeling magneto rheological damper using artificial neural network method /
_cFarah Wahida Yusof
260 _aKuantan, Pahang :
_bUMP,
_c2012
300 _axvi, 46 p. :
_bill. (some col.) ;
_c30 cm. +
_e1 CD-ROM
502 _aProject paper ( Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang - 2012
504 _aBibilography : p. 43-44
520 3 _aThe purpose of this study is to model magneto rheological (MR) damper using the artificial neural network (ANN) method. There are two ways of modeling the MR damper, but throughout this study the non-parametric model is being use. The method chosen is ANN method. The model of MR damper was simulated in Simulink® MATLAB. Firstly, the block diagram to simulate the MR damper was designed. Instead of designing the block diagram, the ANN coding was also being designed. Next, the simulation was run to obtain the damping force predicted by ANN method. The values of learning rates and weights were being tuned to obtain the desired result from ANN method. The result from ANN method was compared with the result collected from the actual experiment. As it is a model, so the exact value of damping force is impossible to obtain. The difference between the actual damping force and the damping force obtain by ANN method will be calculated by plotted the error. RMSE was used to visualize the error. However, at the end of this project period, the smallest value of RMSE was 3.320945. This RMSE value still large if compared to other previous research on ANN method use to model MR damper. The best model should have the minimum RMSE value around 0.018.
650 0 _aDamping (Mechanics)
999 _aVIRTUA40
_c4605
_d4611
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