首页 | 本学科首页   官方微博 | 高级检索  
     检索      

基于BP神经网络的长江口北支河槽容积分析
引用本文:陈 维,顾 杰,李雯婷,秦 欣.基于BP神经网络的长江口北支河槽容积分析[J].海洋科学,2011,35(1):70-74.
作者姓名:陈 维  顾 杰  李雯婷  秦 欣
作者单位:上海海洋大学,海洋科学学院,上海,201306
基金项目:上海市教委重点学科项目(J50702) ; 上海市教育委员会科研创新重点项目(08ZZ81); 上海市科委“创新行动计划”部分地方院校计划项目(08230510700)
摘    要:根据实测水文及泥沙等资料,采用现在较成熟的且应用广泛的BP人工神经网络建立了北支0m以下河槽容积与大通流量、大通输沙量及北支分流比3个因子问的神经网络模型,网络结构为3.1-7-1,通过选择合适的参数,模型训练较好,预测结果与线性回归模型预测结果相近,说明BP神经网络模型能够广泛应用于河口水文等方面的预报.

关 键 词:BP神经网络  长江口北支  河槽容积  北支分流比
收稿时间:1/9/2010 12:00:00 AM
修稿时间:2010/11/4 0:00:00

Analysis of the channel cubage of the North Branch of the Yangtze River Estuary with BP neural network
CHEN Wei,GU Jie,LI Wen-ting,QIN Xin.Analysis of the channel cubage of the North Branch of the Yangtze River Estuary with BP neural network[J].Marine Sciences,2011,35(1):70-74.
Authors:CHEN Wei  GU Jie  LI Wen-ting  QIN Xin
Institution:CHEN Wei,GU Jie,LI Wen-ting,QIN Xin(College of Marine Sciences,Shanghai Ocean University,Shanghai 201306,China)
Abstract:Based on the hydrology and sediment data, an artificial neural network model was established to study the relationship among the channel cubage under the 0 m-isobath in North Branch, the flow and sediment discharge at Datong gauging station and the flow split ratio of the North Branch. The structure of the network model was fixed on 3-1-7-1. The network model was trained and tested by choosing appropriate parameters. The computation results of BP artificial neural network agree well with that of multiple linear regressions. It can be concluded that BP artificial neural network may be used to predict the hydrological factors such as sediment discharge in estuary.
Keywords:BP neural network  North Branch  channel cubage  flow split ratio
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《海洋科学》浏览原始摘要信息
点击此处可从《海洋科学》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号