基于ELM-PNN算法的第24周太阳黑子预测预报

发布时间：2018-06-22 19:31

  本文选题：过程神经网络 + 极限学习　； 参考：《控制与决策》2017年04期

【摘要】：为了提高太阳黑子预测预报的精度,提出固定型极限学习过程神经网络(FELM-PNN)和增量型极限学习过程神经网络(IELM-PNN)两种学习算法.FELM-PNN的隐层节点数目固定,使用SVD求解隐层输出矩阵的Moore-Penrose广义逆,通过最小二乘法计算隐层输出权值;IELM-PNN逐次增加隐层节点,根据隐层输出矩阵和网络误差计算增加节点的输出权值.通过Henon时间序列预测验证了两种方法的有效性,并实际应用于第24周太阳黑子平滑月均值的中长期预测预报中.实验结果表明,两种方法的预测精度均有一定程度的提高,IELM-PNN的训练收敛性优于FELM-PNN.
[Abstract]:In order to improve the accuracy of sunspot prediction, two learning algorithms, fixed limit learning process neural network (FELM-PNN) and incremental limit learning process neural network (IELM-PNN), are proposed. The number of hidden layer nodes of FELM-PNN is fixed. The Moore-Penrose generalized inverse of the hidden layer output matrix is solved by SVD, and the output weight of the hidden layer is calculated by the least square method. IELM-PNN increases the hidden layer node step by step, and the output weight value of the node is calculated according to the hidden layer output matrix and network error. The effectiveness of the two methods is verified by Henon time series prediction and is applied to the medium-long term prediction of sunspot smoothing monthly mean in the 24th cycle. The experimental results show that the prediction accuracy of the two methods is better than that of FELM-PNN to a certain extent, and the training convergence of IELM-PNN is better than that of FELM-PNN.
【作者单位】： 东北石油大学计算机与信息技术学院;山东科技大学信息科学与工程学院;
【基金】：国家自然科学基金项目(61170132) 黑龙江省自然科学基金项目(F2015021)
【分类号】：P182;TP183
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本文编号：2053985