基于BP神经网络的长江口北支河槽容积分析OA北大核心CSCDCSTPCD
Analysis of the channel cubage of the North Branch of the Yangtze River Estuary with BP neural network
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.
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 …查看全部>>
陈维;顾杰;李雯婷;秦欣
上海海洋大学,海洋科学学院,上海,201306上海海洋大学,海洋科学学院,上海,201306上海海洋大学,海洋科学学院,上海,201306上海海洋大学,海洋科学学院,上海,201306
海洋科学
BP神经网络长江口北支河槽容积北支分流比
BP neural network North Branch channel cubage flow split ratio
《海洋科学》 2011 (1)
70-74,5
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)
评论