MARC details
| 000 -LEADER |
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| fixed length control field |
02327nam a2200277 a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
vtls000055164 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251114204436.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
110801t2009 my da f m 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0005495(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
|---|
| Level of rules in bibliographic description |
201905160940 |
| Level of effort used to assign nonsubject heading access points |
hanafiah |
| -- |
201108011214 |
| -- |
Fida |
| 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 .N87 2009 rs Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Siti Nursyuhada Mahsahirun |
| 245 10 - TITLE STATEMENT |
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| Title |
Quantitative precipitation analysis and offline gui using neural network system / |
| Statement of responsibility, etc. |
Siti Nursyuhada Mahsahirun |
| 246 3# - VARYING FORM OF TITLE |
|---|
| Title proper/short title |
Quantitative precipitation analysis and offline gui using neural network system |
| Medium |
[computer file] |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Kuantan, Pahang : |
| Name of publisher, distributor, etc. |
UMP, |
| Date of publication, distribution, etc. |
2009 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvi, 81 p. : |
| Other physical details |
ill. (some col.) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 computer disc |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Project paper (Bachelor of Electrical Engineering (Electronics)) -- Universiti Malaysia Pahang - 2009 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Bibliography : p.50 |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
This project discovers the implementation of Artificial Neural Network (ANN) for forecasting weather based on past relevant data. Neural network is constructed using empirical network architecture and (17) training types. They are such as BFGS quasi-Newton backpropagation, Cyclical order incremental training w/learning functions, Levenberg-Marquardt backpropagation, Resilient backpropagation and others. The ANN has been trained using 2008 weather data and tested with data year 2009. As result, the system has successfully generating accuracy up to 78.69% for quantitative precipitation (QP) prediction. Analysis on time consumption of all those training types is made and shows that Resilient backpropagation with 1.92s training time consumption is the fastest and Cyclical order incremental training w/learning functions with 463.215s is the slowest. This project concluded that ANN is an alternative method in controlling and understanding the way of non-linear set of data and variables to become mutually correlated with each other. It is a powerful yet significant method in embedding intelligent system into application for meteorological tools. |
| 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 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Graphical user interfaces (Computer systems) |