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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| Format: | Undergraduates Project Papers |
| Language: | en |
| Published: |
2009
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| Online Access: | http://umpir.ump.edu.my/id/eprint/1093/1/Kamal_Ariffin_Mohamad.pdf http://umpir.ump.edu.my/id/eprint/1093/ |
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| _version_ | 1831520909621460992 |
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| author | Kamal Ariffin, Mohamad |
| author_facet | Kamal Ariffin, Mohamad |
| author_sort | Kamal Ariffin, Mohamad |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.ump.umpir.1093 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2009 |
| 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 . |
| spellingShingle | QA Mathematics Kamal Ariffin, Mohamad Uncertainty propagation analysis of artificial neural network (ANN) approximated function using numerical and analytical method |
| title | 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_short | 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 |
| topic | QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/1093/1/Kamal_Ariffin_Mohamad.pdf http://umpir.ump.edu.my/id/eprint/1093/ |
| url_provider | http://umpir.ump.edu.my/ |