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.
In order to improve prediction and forecast accuracy for the sunspot number,two process neural network（PNN）training algorithms of fixed extreme learning machine PNN（FELM-PNN） and incremental extreme learning machine PNN（IELM-PNN） are proposed.The FELM-PNN has fixed numbers of hidden layer nodes,and uses singular value decomposition（SVD） to compute Moore-Penrose generalized inverse of the hidden layer output matrix.The hidden layer output weights are solved by using the least squares method.For the IELM-PNN,the hidden layer nodes are added to the model one by one.The output weights for the added node are computed by according to the hidden layer output matrix and the network output error.The effectiveness of the two proposed methods is verified by Henon time series prediction.The two proposed methods are applied to the 24 th cycle sunspot smoothed monthly mean Mid-and-long forecasting problem.The experimental results show that the prediction accuracy of the two methods increased at certain degree,and the training convergence of the IELM-PNN is better than that of the FELM-PNN.
出处
《控制与决策》
EI
CSCD
北大核心
2017年第4期642-646,共5页
Control and Decision
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)
关键词
过程神经网络
极限学习
网络训练
广义逆
太阳黑子数
process neural network
extreme learning
network training
generalized inverse
sunspot numbers
