Cover Image

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...

Full description

Saved in:
Bibliographic Details
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/1093/1/Kamal_Ariffin_Mohamad.pdf
http://umpir.ump.edu.my/id/eprint/1093/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.1093
record_format eprints
spelling my.ump.umpir.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 .
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/1093/1/Kamal_Ariffin_Mohamad.pdf
http://umpir.ump.edu.my/id/eprint/1093/
_version_ 1643664321351254016
score 13.211869

Similar Items