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Application of Gray-self-memory-neural Network Model to Prediction of the Annual Runoff

机译：灰色自记忆神经网络模型在年径流量预测中的应用

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Runoff time series is a non-linear, weakly dependent and complicated dynamic system. The key of improving the accuracy of runoff prediction is to dig the information in the limited sample sufficiently. Gray system modeling uncovers the dynamic laws inside the system via processing gray information in order to transform the desultory data into ordered series for establishing model based on differential equations. Self-memory theory on base of physical motion irreversibility, emphasizes relation of system status, which urges upon evolutional rules of system itself, and then differential equations of dynamic system could be set up for the self-memory models. Combination of gray, self-memory could effectively responds ultra data, but with some phase lag. Neural network has advantage of paralleling distributed processing. On account of integrative prediction, three modeling are combined to forecast annual runoff. It is shown that gray self-memory neural network model has higher prediction accuracy and may be fit for annual runoff prediction.
机译：径流时间序列是一个非线性的，弱相关且复杂的动态系统。提高径流预测精度的关键是充分挖掘有限样本中的信息。灰色系统建模通过处理灰色信息来揭示系统内部的动态规律，以便将稀疏数据转换为有序序列，以便基于微分方程建立模型。基于身体运动不可逆性的自记忆理论，强调系统状态之间的关系，敦促系统自身的演化规律，进而为自记忆模型建立动力系统的微分方程。灰色，自记忆的组合可以有效地响应超数据，但存在一定的相位滞后。神经网络具有并行化分布式处理的优势。由于综合预测，将三个模型结合起来以预测年径流量。结果表明，灰色的自记忆神经网络模型具有较高的预测精度，可用于年径流量预测。

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