Uncertainty analysis for the unknown function using artificial neural network (ANN) approximated function

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 jo...

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Main Author:	Siti Hajar, Mohd Noh
Format:	Undergraduates Project Papers
Language:	English
Published:	2010
Subjects:	QA Mathematics
Online Access:	http://umpir.ump.edu.my/id/eprint/1909/ http://umpir.ump.edu.my/id/eprint/1909/ http://umpir.ump.edu.my/id/eprint/1909/1/Siti_Hajar_Mohd_Noh_%28_CD_4965_%29.pdf

id	ump-1909
recordtype	eprints
spelling	ump-19092015-03-03T07:53:31Z http://umpir.ump.edu.my/id/eprint/1909/ Uncertainty analysis for the unknown function using artificial neural network (ANN) approximated function Siti Hajar, Mohd Noh QA Mathematics 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. 2010-12 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1909/1/Siti_Hajar_Mohd_Noh_%28_CD_4965_%29.pdf Siti Hajar, Mohd Noh (2010) Uncertainty analysis for the unknown function using artificial neural network (ANN) approximated function. Faculty of Mechanical Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:51475&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Siti Hajar, Mohd Noh Uncertainty analysis for the unknown function using artificial neural network (ANN) approximated function
description	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.
format	Undergraduates Project Papers
author	Siti Hajar, Mohd Noh
author_facet	Siti Hajar, Mohd Noh
author_sort	Siti Hajar, Mohd Noh
title	Uncertainty analysis for the unknown function using artificial neural network (ANN) approximated function
title_short	Uncertainty analysis for the unknown function using artificial neural network (ANN) approximated function
title_full	Uncertainty analysis for the unknown function using artificial neural network (ANN) approximated function
title_fullStr	Uncertainty analysis for the unknown function using artificial neural network (ANN) approximated function
title_full_unstemmed	Uncertainty analysis for the unknown function using artificial neural network (ANN) approximated function
title_sort	uncertainty analysis for the unknown function using artificial neural network (ann) approximated function
publishDate	2010
url	http://umpir.ump.edu.my/id/eprint/1909/ http://umpir.ump.edu.my/id/eprint/1909/ http://umpir.ump.edu.my/id/eprint/1909/1/Siti_Hajar_Mohd_Noh_%28_CD_4965_%29.pdf
first_indexed	2023-09-18T21:55:14Z
last_indexed	2023-09-18T21:55:14Z
_version_	1777414035062390784

Uncertainty analysis for the unknown function using artificial neural network (ANN) approximated function

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