灰色神经网络在城市用水量预测中的应用  

Application of Gray Neutral Network to Prediction of Urban Water Quantity

作　　者：胡瑜[1] 于宝堃[1] 许国[1] 张莹[1] 

机构地区：[1]天津科技大学电子信息与自动化学院,天津300222

出　　处：《微电子学与计算机》2012年第7期142-145,149,共5页Microelectronics & Computer

基　　金：国家"十一五"科技支撑计划项目(2006BAJ16B08)

摘　　要：为解决传统BP神经网络在城市用水量预测中易陷入局部极小点等问题,将BP神经网络与灰色理论相结合,构建了灰色神经网络模型(GNNM),实现了二者的优势互补,并利用粒子群优化算法(PSO)对该模型的初始权值和阈值进行优化,形成了PSO-GNNM(1,N)算法.通过与传统BP神经网络、灰色理论预测法的预测结果相比较,该算法具有预测误差小、泛化能力强等优点,可为城市用水量的预测工作提供技术支持.In order to overcome traditional BP neural network＇s problem of trapping to a local optimum in prediction of urban water quantity,the gray neutral network model（GNNM） was created by combining BP neural network with gray theory,which can realize the mutual supplement with advantages of these two methods.The PSO-GNNM（1,N） algorithm was formed by using particle swarm optimization to optimize initialized weights and threshold of GNNM.By comparison with the predicted results of traditional BP neural network and gray theory forecasting method,this algorithm had little prediction error and better generalization performance,which can provide technical support for prediction of urban water quantity.

关 键 词：城市用水量 灰色神经网络 粒子群优化算法 PSO-GNNM(1 N)算法 

分 类 号：TU991[建筑科学—市政工程]