基于改进BP神经网络砾石土-滤层渗透破坏判别研究  

Study of Improved BP(Error Back-Propagation)-based Neutral Network Gravel Soil—Filter Seepage Failure Discrimination

作　　者：卢建移[1] 段波[1] 

机构地区：[1]四川大学水利水电学院,四川成都610065

出　　处：《云南水力发电》2011年第5期4-6,22,共4页Yunnan Water Power

摘　　要：针对BP人工神经网络具有易陷入局部极小、学习过程中常常发生振荡等缺陷,提出了在BP算法中引入动量因子,并采用自适应调整学习率的梯度下降算法,建立了砾石土渗透破坏判别的改进BP神经网络模型。根据砾石土渗透破坏的实测资料,分别对BP神经网络判别结果和改进的BP神经网络判别结果进行比较,结果表明后者比前者判别能力更佳。In consideration of that BP artificial neutral network is apt to cause local minimization instead of whole minimization and to lower learning rate, etc., the paper has established the improved BP neutral network model for discrimination of gravel seepage failure by introducing momenturm factors into the BP algorithm and adopting the gradient method of self-adaptive regulating learning rate. A comparison of the results obtained using the BP neutral network and the improved BP neutral network, according to the measurement of gravel soil seepage failure, indicates that the latter is of higher capability of discrimination than the former.

关 键 词：BP神经网络 动量因子 自适应学习率 渗透破坏 

分 类 号：TV640.31[水利工程—水利水电工程] TV640.33