| 000 | 02327nam a2200277 a 4500 | ||
|---|---|---|---|
| 001 | vtls000055164 | ||
| 003 | KUKTEM | ||
| 005 | 20251114204436.0 | ||
| 008 | 110801t2009 my da f m 000 0 eng d | ||
| 020 | _aTHE0005495(Local) | ||
| 039 | 9 |
_a201905160940 _bhanafiah _y201108011214 _zFida |
|
| 040 | _aUMP | ||
| 090 | _aQA76.87 .N87 2009 rs Bc. | ||
| 100 | 0 | _aSiti Nursyuhada Mahsahirun | |
| 245 | 1 | 0 |
_aQuantitative precipitation analysis and offline gui using neural network system / _cSiti Nursyuhada Mahsahirun |
| 246 | 3 |
_aQuantitative precipitation analysis and offline gui using neural network system _h[computer file] |
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| 260 |
_aKuantan, Pahang : _bUMP, _c2009 |
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| 300 |
_axvi, 81 p. : _bill. (some col.) ; _c30 cm. + _e1 computer disc |
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| 502 | _aProject paper (Bachelor of Electrical Engineering (Electronics)) -- Universiti Malaysia Pahang - 2009 | ||
| 504 | _aBibliography : p.50 | ||
| 520 | 3 | _aThis 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 | _aNeural networks (Computer science) | |
| 650 | 0 | _aSystem analysis | |
| 650 | 0 | _aGraphical user interfaces (Computer systems) | |
| 999 |
_aVIRTUA40 _c2108 _d2114 |
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| 999 | _aVTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5040*5200*6500*6501*6502*9992 | ||