02948nam a2200265 a 4500 vtls000051475 KUKTEM 20251114204502.0 110224t2010 my da f m 000 0 eng d THE0005492(Local) 201905160937 hanafiah 201107140034 VLOAD 201102241244 ida UMP QA76.87 .H35 2010 rs Bc. Siti Hajar Mohd Noh Uncertainty analysis for the unknown function using artificial neural network (ANN) approximated function / Siti Hajar Bte Mohd Noh Uncertainty analysis for the unknown function using artificial neural network (ANN) approximated function [electronic resource] Kuantan, Pahang : UMP, 2010 xv, 66 p. : ill. (some col.) ; 30 cm. + 1 computer disc Project paper (Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang - 2010 Bibliography : p.59 This 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. Neural networks (Computer science) System analysis 0 0 0 1 20000 20000 2019-09-04 0 QA76.87 .H35 2010 rs Bc. 0000053899 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 20000 20000 2019-09-04 0 CD 4965 | QA76.87 .H35 2010 rs Bc. 0000053900 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 2834 2840 VTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5040*5200*6500*6501*9992