利用“太阳黑子神经预报”系统用循环神经网络(RNN)预测太阳黑子数

R. Samin, R. Kasmani, A. Khamis, Syahirbanun Isa
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引用次数: 3

摘要

本文研究了不同类型的递归神经网络(RNN)在预测太阳黑子数中的预报性能。递归神经网络将在本研究中使用不同的学习算法,太阳黑子数据模型和RNN传递函数。仿真使用Matlab 7完成,其中定制的图形用户界面(GUI)称为“太阳黑子神经预报器”已经开发用于分析。从均方误差(MSE)和相关分析的角度对不同的学习算法、太阳黑子数据模型和RNN传递函数进行了全面的分析。最后,将优化后的最佳RNN参数用于预测太阳黑子数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting Sunspot Numbers with Recurrent Neural Networks (RNN) Using 'Sunspot Neural Forecaster' System
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 ‘Sunspot Neural Forecaster’ 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.
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