MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02948nam a2200265 a 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
vtls000051475 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251114204502.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
110224t2010 my da f m 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0005492(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
|---|
| Level of rules in bibliographic description |
201905160937 |
| Level of effort used to assign nonsubject heading access points |
hanafiah |
| Level of effort used to assign subject headings |
201107140034 |
| Level of effort used to assign classification |
VLOAD |
| -- |
201102241244 |
| -- |
ida |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMP |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
QA76.87 .H35 2010 rs Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Siti Hajar Mohd Noh |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Uncertainty analysis for the unknown function using artificial neural network (ANN) approximated function / |
| Statement of responsibility, etc. |
Siti Hajar Bte Mohd Noh |
| 246 3# - VARYING FORM OF TITLE |
|---|
| Title proper/short title |
Uncertainty analysis for the unknown function using artificial neural network (ANN) approximated function |
| Medium |
[electronic resource] |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Kuantan, Pahang : |
| Name of publisher, distributor, etc. |
UMP, |
| Date of publication, distribution, etc. |
2010 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xv, 66 p. : |
| Other physical details |
ill. (some col.) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 computer disc |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Project paper (Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang - 2010 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Bibliography : p.59 |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. |
| 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 |
System analysis |