| 000 | 01747ntm a2200241 a 4500 | ||
|---|---|---|---|
| 001 | vtls000072062 | ||
| 003 | KUKTEM | ||
| 005 | 20251114204536.0 | ||
| 008 | 130617t2012 my da f m 000 0 eng d | ||
| 020 | _aTHE0007373(Local) | ||
| 039 | 9 |
_a201905151047 _bfawwaz _c201306171453 _dhuda _y201306171445 _zhuda |
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| 040 | _aUMP | ||
| 090 | _aTL574.S7 A35 2012 rs Bc. | ||
| 100 | 0 | _aMuhammad Afiq Naquiddin Abd Rahman | |
| 245 | 1 |
_aModeling the magneto- rheological damper using recurrent neural network method / _cMuhammad Afiq Naquiddin Abd Rahman |
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| 260 |
_aKuantan, Pahang : _bUMP, _c2012 |
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| 300 |
_axiv, 47 p. : _bill. (some col.) ; _c30 cm. + _e1 CD-ROM |
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| 502 | _aProject paper (Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang - 2012 | ||
| 504 | _aBibliography : p. 46-47 | ||
| 520 | 3 | _aThis thesis is study about modeling the Magnetorheological damper using Recurrent Neural Network 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 0.4008, while the highest RMSE is 1.9882. From the results also, the better current value to modeling the MR damper is using the MR damper with the lowest RMSE. | |
| 650 | 0 | _aDamping (Mechanics) | |
| 999 |
_aVIRTUA40 _c3844 _d3850 |
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| 999 | _aVTLSSORT0080*0200*0400*0900*1000*2450*2600*3000*5020*5040*5200*6500*9992 | ||