MARC details
| 000 -LEADER |
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| fixed length control field |
02876nam a2200265 a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
vtls000045866 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251114204425.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
100521t2009 my a f m 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0005493(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
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| Level of rules in bibliographic description |
201905160938 |
| Level of effort used to assign nonsubject heading access points |
hanafiah |
| Level of effort used to assign subject headings |
201107132320 |
| Level of effort used to assign classification |
VLOAD |
| -- |
201005211025 |
| -- |
ida |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMP |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
QA76.87 .J85 2009 rs Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Mohd Jukimi Joni |
| 245 10 - TITLE STATEMENT |
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| Title |
Uncertainty analysis of artificial neural network (ANN) aproximated function for experimental data using sequential perturbation method / |
| Statement of responsibility, etc. |
Mohd Jukimi Bin Joni |
| 246 3# - VARYING FORM OF TITLE |
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| Title proper/short title |
Uncertainty analysis of artificial neural network (ANN) aproximated function for experimental data using sequential perturbation method [electronic resource] |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Kuantan, Pahang : |
| Name of publisher, distributor, etc. |
UMP, |
| Date of publication, distribution, etc. |
2009 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xv, 64 p. : |
| Other physical details |
ill. (some col.) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 computer disc |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Project paper (Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang - 2009 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Bibliography : p. 58 |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
This thesis describes a comparative study of uncertainty estimation for unknown function using sequential perturbation method with Artificial Neural Network (ANN) approximated function. The objective of this project is to propose a new technique in calculating uncertainty estimation for an unknown function which is data obtains from experimental or measurement. For this research of the uncertainty analysis can be applied to calculate uncertainty value for the experiment data that not have function. The process to determine uncertainty have six step including begin from selected experiment function, generate the experiment data, function approximation using ANN, calculate the uncertainty for analytical method manually, applied the sequential perturbation method with ANN and lastly determine percent error between sequential perturbation method with ANN compare with the analytical method. Meanwhile, the variation of uncertainty error for Sequential Perturbation method without ANN is 0.0510%, but the error of sequential perturbation method with The ANN is 0.1559%. Then compare the value of Sequential Perturbation (numerical) method with ANN and value of Analytical method to validate the data. The new technique will be approving to determine the uncertainty analysis using combination of Sequential Perturbation method with artificial neural network (ANN). Any experiment also can be use, the applications of Sequential Perturbation method with ANN propose in this study. Consequently it implies the application of Sequential Perturbation method is a good as the application of the analytical method in order to calculate the propagation of uncertainty. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Neural networks (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Thermodynamics |