应用LM算法的神经网络模型研究灌区退水问题

    Return water of irrigation area using neural network model based on LM Algorithm

    • 摘要: 在一些引黄灌区中,灌溉引水的相当大部分要转化为退水回归黄河,灌区退水研究对这部分水量的重新利用有着重要的意义。该文采用相关分析的方法确定了灌区退水的主要影响因素,应用LM算法的神经网络模型对灌区退水的量化分析方法进行了探讨。实例研究表明,模型能够较准确的对灌区退水量进行模拟和预测,对灌区退水问题研究具有较好的应用价值。

       

      Abstract: In some Yellow River irrigation areas, most of irrigation water returns to its source as return water and the study of return water has a significant meaning to reuse it. The paper determines the major factors on the return water using correlation analysis method and discusses the quantitative analysis method of return water with the neural network model based on Levenberg-Marquardt algorithm. Through studying examples, the model can accurately simulate and predict the return water volume and has the application value in the study of return water volume of irrigation area.

       

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