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Forecasting Sunspot Numbers with Recurrent Neural Networks (RNN) Using 'Sunspot Neural Forecaster' System

机译:使用“黑子神经预报器”系统通过递归神经网络(RNN)预测黑子数

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This paper presents the investigations of forecasting performance of different type of Recurrent Neural Networks (RNN) in forecasting the sunspot numbers. Recurrent Neural Network will be used in this investigation by using different learning algorithms, sunspot data models and RNN transfer functions. Simulations are done using Matlab 7 where customized Graphic User Interface (GUI) called ȁ8;Sunspot Neural Forecasterȁ9; have been developed for analysis. A complete analysis for different learning algorithms, sunspot data models and RNN transfer functions are examined in terms of Mean Square Error(MSE) and correlation analysis. Finally, the best optimized RNN parameters will be used to forecast the sunspot numbers.
机译:本文介绍了对不同类型的递归神经网络(RNN)预测黑子数的性能的研究。通过使用不同的学习算法,黑子数据模型和RNN传递函数,将在此调查中使用递归神经网络。使用Matlab 7进行仿真,其中定制的图形用户界面(GUI)称为ȁ8; Sunspot Neural Forecasterȁ9;已开发用于分析。根据均方误差(MSE)和相关分析,对不同的学习算法,黑子数据模型和RNN传递函数进行了完整的分析。最后,将使用最佳优化的RNN参数来预测黑子数。

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