DocumentCode :
2296572
Title :
Forecasting sunspot numbers with Feedforward Neural Networks (FNN) using ‘Sunspot Neural Forecaster’ system
Author :
Samin, Reza Ezuan ; Saealal, Muhammad Salihin ; Khamis, Azme ; Isa, Syahirbanun ; Kasmani, Ruhaila Md
Author_Institution :
Fac. of Electr. & Electron. Eng., Univ. Malaysia Pahang, Pekan, Malaysia
fYear :
2011
fDate :
21-22 June 2011
Firstpage :
1
Lastpage :
5
Abstract :
This paper presents the investigations of forecasting performance of different type of Feedforward Neural Networks (FNN) in forecasting the sunspot numbers. Feedforward Neural Network will be used in this investigation by using different learning algorithms, sunspot data models and FNN transfer functions. Simulations are done using Matlab 7 where customized Graphic User Interface (GUI) called `Sunspot Neural Forecaster´ have been developed for analysis. A complete analysis for different learning algorithms, sunspot data models and FNN transfer functions are examined in terms of Mean Square Error (MSE) and correlation analysis. Finally, the best optimized FNN parameters will be used to forecast the sunspot numbers.
Keywords :
astronomy computing; feedforward neural nets; graphical user interfaces; learning (artificial intelligence); mathematics computing; sunspots; FNN transfer functions; Matlab 7 simulation; correlation analysis; feedforward neural networks; graphic user interface; learning algorithms; mean square error; sunspot data models; sunspot neural forecaster system; sunspot number forecasting; Algorithm design and analysis; Analytical models; Artificial neural networks; Correlation; Forecasting; Mathematical model; Transfer functions; Feedforward Neural Networks (FNN); Mean Square Error (MSE); Sunspot numbers;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical, Control and Computer Engineering (INECCE), 2011 International Conference on
Conference_Location :
Pahang
Print_ISBN :
978-1-61284-229-5
Type :
conf
DOI :
10.1109/INECCE.2011.5953839
Filename :
5953839
Link To Document :
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