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Study on resource quantity of surface water based on phase space reconstruction and neural network

机译:基于相空间重构和神经网络的地表水资源量研究

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Proposed a new method to disclose the complicated non-linearity structure of the water-resource system, introducing chaos theory into the hydrology and water resources field, and combined with the chaos theory and artificial neural networks. Training data construction and networks structure were determined by the phase space reconstruction, and establishing nonlinear relationship of phase points with neural networks, the forecasting model of the resource quantity of the surface water was brought forward. The keystone of the way and the detailed arithmetic of the network training were given. The example shows that the model has highly forecasting precision.
机译:提出了一种揭示水资源系统复杂非线性结构的新方法,将混沌理论引入水文学和水资源领域,并结合混沌理论和人工神经网络。通过相空间重构确定训练数据的结构和网络结构,利用神经网络建立相点的非线性关系,提出了地表水资源量的预测模型。给出了方法的重点和网络训练的详细算法。实例表明,该模型具有较高的预测精度。

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