径向基函数神经网络需水预测研究  

Water Demand Prediction Based on Radial Basis Function Neural Network

作　　者：刘俊萍[1] 畅明琦 

机构地区：[1]浙江工业大学建筑工程学院,浙江杭州310032 [2]山西省水资源研究所,山西太原030001

出　　处：《水文》2007年第5期12-15,11,共5页Journal of China Hydrology

基　　金：国家重点基础研究发展规划资助项目(G1999043608)

摘　　要：在分析山西省历年用水量和人均用水量的基础上,建立径向基函数神经网络需水预测模型,采用最近邻聚类学习算法确定径向基函数的宽度、选取聚类中心和权值。采用丰富的需水预测因子作为模型的输入,网络输出需水预测值。预测结果表明,径向基函数神经网络需水预测模型运算速度快,有较高的预测精度。需水预测可为水资源规划和配置提供依据。Based on analysis of the water consumption and water consumption per capita in Shanxi Province for years, the water demand prediction model of radial basis function neural network was set up. The nearest neighbor-clustering algorithm was adopted to decide the width of radial basis function, the cluster centers were chosen, and the weight values were calculated. Abundant water demand predicting factors were used as the input data of the model, and the RBF neural network output the water demand predicting values. The predicting results show that the model has faster calculating speed and higher predicting accuracy, which can provide basis for water resources planning and allocation.

关 键 词：径向基函数 神经网络 需水预测 最近邻聚类算法 

分 类 号：TV213.4[水利工程—水文学及水资源]