自适应BP算法及其在河道洪水预报上的应用  

River flow prediction using artificial neural networks: self-adaptive error back-propagation algorithm

作　　者：覃光华[1] 丁晶[1] 刘国东[1] 

机构地区：[1]四川大学水电学院,四川成都610065

出　　处：《水科学进展》2002年第1期37-41,共5页Advances in Water Science

基　　金：国家自然科学基金重大项目 ( 5 0 0 996 2 0 )~~

摘　　要：提出一种改进的BP算法 ,即自适应BP算法。该方法采用两种策略 :一是在权重修改公式中加动量项 ;二是学习率随总误差的变化作自适应调整 ,亦即总误差增加时 ,学习率将减小 ,反之学习率增大。以上两种策略能有效的抑制网络陷于局部极小并缩短了学习时间。实例研究表明 ,该算法用于河道洪水的预报 。This paper presents a improved error back propagation(BP) algorithm, which is named as self adaptive error BP algorithm The method includes two strategies: one is adding momentum term at the iterative expressions of the weights,the other is self adaptive adjustment of the learning rate according to the variety of the sum error Namely, if the sum error increases, the learning rate will decrease,conversely, the sum error decreases, the learning rate will increases The learning rate is changed until the sum error has reached a satisfactory level The improved algorithm can prevent the networks from getting in the part least and can shorten the study time The self adaptive error BP algorithm was utilized for predicting river flow of Yangtse River at Yichang station, and satisfactory results were acquired

关 键 词：神经网络 自适应BP算法 洪水预报 

分 类 号：P338.6[天文地球—水文科学]