THE RAINFALL FORECAST MODEL OF PCA-RBF NEURAL NETWORKS BASED ON MATLAB
THE RAINFALL FORECAST MODEL OF PCA-RBF NEURAL NETWORKS BASED ON MATLAB
复制标题
DOI：
--
复制
发表时间：
2008
影响因子：
3
通讯作者：
Nong Ji-fu
中科院分区：
文献类型：
--
作者：
Nong Ji-fu
利用前期500 hPa高度场和海温资料，采用RBF神经网络技术和主成分分析（PCA）方法，建立了桂中地区5月平均降水量的预报模型，对5年样本的预报试验表明，平均相对误差为18.12%，均方根误差为50.52%，平均绝对误差为34.23。与BP神经网络模型相比，RBF神经网络模型的预测结果更准确。
Based on previous 500 hPa geopotential height and sea surface temperatures,a prediction model of the monthly mean rainfall in May for the central part of Guangxi is established with RBF neural network technology and principal component analysis(PCA) method.The results of the forecast experiment with 5-year samples indicate that the mean relative error is 18.12%,the root mean square error is 50.52,and the mean absolute error is 34.23.The prediction results of RBF neural network are proved to be more accurate compared with BP neural network model.