02327nam a2200277 a 4500 vtls000055164 KUKTEM 20251114204436.0 110801t2009 my da f m 000 0 eng d THE0005495(Local) 201905160940 hanafiah 201108011214 Fida UMP QA76.87 .N87 2009 rs Bc. Siti Nursyuhada Mahsahirun Quantitative precipitation analysis and offline gui using neural network system / Siti Nursyuhada Mahsahirun Quantitative precipitation analysis and offline gui using neural network system [computer file] Kuantan, Pahang : UMP, 2009 xvi, 81 p. : ill. (some col.) ; 30 cm. + 1 computer disc Project paper (Bachelor of Electrical Engineering (Electronics)) -- Universiti Malaysia Pahang - 2009 Bibliography : p.50 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. Neural networks (Computer science) System analysis Graphical user interfaces (Computer systems) 0 0 0 1 20000 20000 2019-09-04 0 QA76.87 .N87 2009 rs Bc. 0000058358 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 20000 20000 2019-09-04 0 CD 5362 | QA76.87 .N87 2009 rs Bc. 0000058359 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 2108 2114 VTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5040*5200*6500*6501*6502*9992