Forecasting sunspot numbers with Feedforward Neural Networks (FNN) using 'sunspot neural forecaster' system

• Main Authors: Reza Ezuan, Samin, Ahmad Salihin, Samsudin, Azme, Khamis, Syahirbanun, Isa, Ruhaila, Md. Kasmani
• Format: Conference or Workshop Item
• Language: English
• Published: IEEE 2011
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/26211/1/Forecasting%20sunspot%20numbers%20with%20Feedforward%20Neural%20Networks%20%28FNN%29.pdf; http://umpir.ump.edu.my/id/eprint/26211/; https://doi.org/10.1109/INECCE.2011.5953839

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.