Uncertainty propagation analysis of artificial neural network (ANN) approximated function using numerical and analytical method

This thesis is to investigate the uncertainty analysis using numerical sequential perturbation method and analytical Newton approximation method. The objective of this project to propose the a new technique using numerical sequential perturbation in calculating uncertainty propagation compare to the...

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Main Author:	Kamal Ariffin, Mohamad
Format:	Undergraduates Project Papers
Language:	English
Published:	2009
Subjects:	QA Mathematics
Online Access:	http://umpir.ump.edu.my/id/eprint/1093/ http://umpir.ump.edu.my/id/eprint/1093/1/Kamal_Ariffin_Mohamad.pdf

id	ump-1093
recordtype	eprints
spelling	ump-10932015-03-03T07:48:44Z http://umpir.ump.edu.my/id/eprint/1093/ Uncertainty propagation analysis of artificial neural network (ANN) approximated function using numerical and analytical method Kamal Ariffin, Mohamad QA Mathematics This thesis is to investigate the uncertainty analysis using numerical sequential perturbation method and analytical Newton approximation method. The objective of this project to propose the a new technique using numerical sequential perturbation in calculating uncertainty propagation compare to the use of analytical Newton approximation method in application where the unknown function is approximated using artificial neural network ANN. The process to determine uncertainty have five step including begin from selected function, randomize the data, function approximation and applied the numerical method in ANN and lastly determine percent of error between numerical with ANN and compare with the analytical method. The ANN was applied in MATLAB software. From the uncertainty analysis, was define that three major figure the end of this project. First figure shown the average error between numerical and analytical method without ANN are 0.03%. Second figure average error of function approximate the mass flow rate compare the actual value is 0.03%. The application with numerical method with ANN gives small uncertainty propagation error compare with analytical method where the error is 1.2% is the last graph of this project. The new technique will be approving to determine the uncertainty analysis using artificial neural network (ANN). This technique also can be applied for application in laboratory or industrial field. 2009-11 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1093/1/Kamal_Ariffin_Mohamad.pdf Kamal Ariffin, Mohamad (2009) Uncertainty propagation analysis of artificial neural network (ANN) approximated function using numerical and analytical method. Faculty of Mechanical Engineering, Universiti Malaysia Pahang .
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 Kamal Ariffin, Mohamad Uncertainty propagation analysis of artificial neural network (ANN) approximated function using numerical and analytical method
description	This thesis is to investigate the uncertainty analysis using numerical sequential perturbation method and analytical Newton approximation method. The objective of this project to propose the a new technique using numerical sequential perturbation in calculating uncertainty propagation compare to the use of analytical Newton approximation method in application where the unknown function is approximated using artificial neural network ANN. The process to determine uncertainty have five step including begin from selected function, randomize the data, function approximation and applied the numerical method in ANN and lastly determine percent of error between numerical with ANN and compare with the analytical method. The ANN was applied in MATLAB software. From the uncertainty analysis, was define that three major figure the end of this project. First figure shown the average error between numerical and analytical method without ANN are 0.03%. Second figure average error of function approximate the mass flow rate compare the actual value is 0.03%. The application with numerical method with ANN gives small uncertainty propagation error compare with analytical method where the error is 1.2% is the last graph of this project. The new technique will be approving to determine the uncertainty analysis using artificial neural network (ANN). This technique also can be applied for application in laboratory or industrial field.
format	Undergraduates Project Papers
author	Kamal Ariffin, Mohamad
author_facet	Kamal Ariffin, Mohamad
author_sort	Kamal Ariffin, Mohamad
title	Uncertainty propagation analysis of artificial neural network (ANN) approximated function using numerical and analytical method
title_short	Uncertainty propagation analysis of artificial neural network (ANN) approximated function using numerical and analytical method
title_full	Uncertainty propagation analysis of artificial neural network (ANN) approximated function using numerical and analytical method
title_fullStr	Uncertainty propagation analysis of artificial neural network (ANN) approximated function using numerical and analytical method
title_full_unstemmed	Uncertainty propagation analysis of artificial neural network (ANN) approximated function using numerical and analytical method
title_sort	uncertainty propagation analysis of artificial neural network (ann) approximated function using numerical and analytical method
publishDate	2009
url	http://umpir.ump.edu.my/id/eprint/1093/ http://umpir.ump.edu.my/id/eprint/1093/1/Kamal_Ariffin_Mohamad.pdf
first_indexed	2023-09-18T21:53:56Z
last_indexed	2023-09-18T21:53:56Z
_version_	1777413952549945344

Uncertainty propagation analysis of artificial neural network (ANN) approximated function using numerical and analytical method

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