PCA-BP神经网络在降水预测中的应用研究

摘要

该文提出了一种基于主成分分析（PCA）与误差反向传播（BP）神经网络的降水预测模型。首先,通过主成分分析法对降水的主要影响因素进行主成分提取,选取贡献率大于85%的5个主成分来代替原来的14个影响因素,以消除原始输入数据的相关性,解决神经网络建模时输入变量过多、网络规模过大导致效率下降的问题;然后,以主成分分析结果为输入建立降水BP神经网络预测模型。仿真结果表明PCA-BP神经网络模型性能优于传统BP神经网络,能够满足降水预测的要求。%This paper presented a rainfall forecast model based on PCA and BP neural network.Firstly, five main factors were extract ed to replace fourteen original factors affecting rainfall by means of principal component analysis when variance contribution is more than 85%. By which , the correlation of the initial input layer data was eliminated so that the problem of efficiency caused by too many input parameters and by too large network scale in neural network modeling could be solved. And then, the rainfall forecast model was built through taking the results of PCA as inputs of the BP neural network. Simulation results show that the ＇PCABP neural network model is superior to traditional BP neural network, and meet the requirement for rainfall prediction.