02086nam a2200229 a 4500001001400000003000700014005001700021008004100038020002200079040000800101100003100109245011500140246010200255260003400357300006400391502010700455504002400562520116200586650003901748650002001787650004901807vtls000055164KUKTEM20251114204436.0110801t2009    my da  f m    000 0 eng d  aTHE0005495(Local)  aUMP0 aSiti Nursyuhada Mahsahirun10aQuantitative precipitation analysis and offline gui  using neural network system /cSiti Nursyuhada Mahsahirun3 aQuantitative precipitation analysis and offline gui  using neural network systemh[computer file]  aKuantan, Pahang :bUMP,c2009  axvi, 81 p. :bill. (some col.) ;c30 cm. +e1 computer disc  aProject paper (Bachelor of Electrical  Engineering (Electronics)) -- Universiti Malaysia Pahang - 2009  aBibliography : p.503 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. 0aNeural networks (Computer science) 0aSystem analysis 0aGraphical user interfaces (Computer systems)