000			02948nam a2200265 a 4500
001			vtls000051475
003			KUKTEM
005			20251114204502.0
008			110224t2010 my da f m 000 0 eng d
020			_aTHE0005492(Local)
039		9	_a201905160937 _bhanafiah _c201107140034 _dVLOAD _y201102241244 _zida
040			_aUMP
090			_aQA76.87 .H35 2010 rs Bc.
100	0		_aSiti Hajar Mohd Noh
245	1	0	_aUncertainty analysis for the unknown function using artificial neural network (ANN) approximated function / _cSiti Hajar Bte Mohd Noh
246	3		_aUncertainty analysis for the unknown function using artificial neural network (ANN) approximated function _h[electronic resource]
260			_aKuantan, Pahang : _bUMP, _c2010
300			_axv, 66 p. : _bill. (some col.) ; _c30 cm. + _e1 computer disc
502			_aProject paper (Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang - 2010
504			_aBibliography : p.59
520	3		_aThis thesis deals with the finding of uncertainty analysis for the unknown function from experimental data by using Neural Network Approximation. The objective of this thesis is to estimates the uncertainty value for the unknown function where Artificial Neural Network (ANN) approximated function join together with sequential perturbation method will be applied. The thesis describes the uncertainty analysis techniques which are analytical (Newton Approximation) method and numerical (Sequential Perturbation) method to predict the uncertainty value and build up the new function from the experimental data via Fortran program using non-linear regression. The approach in analyzing uncertainty of Nusselt number is approximate the function via ANN using feed-forward and backpropagation network with four inputs and output were randomly generated. Finally, uncertainty outcome through sequential perturbation with ANN will be compare with the outcome using analytical method. Percentage error between both methods shall be compute to prove that uncertainty analysis for unknown function using sequential perturbation with ANN can also be use. From the results, average percentage error between Newton approximation (analytical method) and sequential perturbation (numerical method) retrieved is 5.52395×10-4 %. Meanwhile, the average percentage error between actual Nusselt number produced and approximated Nusselt number is 0.955373 %. However the main focus of this study is to determine whether sequential perturbation with ANN approximated function can be apply or not to estimate the uncertainty for the unknown function. The average percentage error between sequential perturbation with ANN and Newton approximation (analytical method) is 3.563%. Therefore, the objective is achieved.
650		0	_aNeural networks (Computer science)
650		0	_aSystem analysis
999			_aVIRTUA40 _c2834 _d2840
999			_aVTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5040*5200*6500*6501*9992