基于径向基神经网络的月降水量预测模型研究

摘要

针对月降水量高度非线性的特点,以合肥20年的月降水量为时间序列,综合运用径向基函数( RBF)神经网络,建立了一种基于径向基函数的神经网络预测模型。首先对RBF神经网络进行介绍,并将该网络应用于月降水量预测,应用归一化方法对原始数据进行预处理；然后运用MATLAB R2008神经网络工具箱函数建立月降水量预测模型；最后进行仿真实验与分析,将RBF神经网络与传统的BP网络训练预测结果进行比较。结果显示,RBF神经网络模型训练的迭代次数和训练时间、预测结果明显好于传统BP神经网络。%Owing to the strong nonlinearity of monthly rainfall,taking 1990~2010 monthly rainfall data in the Hefei area as the time se-ries and using the RBF neural network,a new monthly forecast model is developed based on RBF neural network. Firstly,introduce the structure of RBF neural networks and discuss the RBF neural networks application for predicting the monthly rainfall. And then,the func-tions of MATLAB toolbox are adopted to create a network model for the monthly rainfall. Finally,RBF neural network and traditional BP network are compared in their prediction results each other through simulation experiments and studies. Simulation results show that the RBF neural network model is superior to traditional BP neural network.