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Forecasting sunspot numbers using neural network : effect to the electrical system

Samin , Reza Ezuan (2006) Forecasting sunspot numbers using neural network : effect to the electrical system. Masters thesis, Kolej Universiti Teknologi Tun Hussein Onn.


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The purpose of this research is to develop the forecasting system of Sunspot Numbers that highly related to Geomagnetic Induced Current (GIC). This geomagnetic induced current (GIC) have the effect to the electrical system especially to the transformers. Sunspot data obtained from the National Geophysical Data Center (NGDC) ranging from 1700 until 2005 is analyzed using Neural Network (NN) using the MATLAB 7.0 Graphic User Interface (GUI) method computer program called "Sunspot Neural Forecaster" so that the analysis and simulation of the sunspot data can be done easily and more user friendly. First, a comparison analysis between Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN) is done to choose the best NN type for the next analysis. The second stage of the analysis involved the selection of NN training algorithm between Levenberg Marquardt, Resilient Backpropagation and Gradient Descent. As in the selection of NN type analysis, the best NN training algorithm is selected for the next analysis. The next analysis involved the selection of NN models between NN1, NN2, NN3 and NN4 and the best models is selected for the last analysis which is the transfer function analysis. The NN transfer function analysis involved Tansig/Purelin and Logsig/Purelin transfer function for the hidden layer and output layer respectively. Based from the analysis that have been done, FNN using Levenberg Marquardt training algorithm with NN2 model and Tansig/Purelin transfer function are used for forecasting the sunspot data. The forecasting result obtained shows the system managed to forecast the sunspot numbers.

Item Type: Thesis (Masters)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Depositing User: Ms Aryanti Ahmad
Date Deposited: 06 Apr 2011 01:03
Last Modified: 29 Apr 2011 06:41
URI: http://eprints.uthm.edu.my/id/eprint/817
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