Considering the influence of dynamical factor of sediment discharge as well as the difficulty of calculating number of input cell, the model is provided with strong nonlinear mapping capacity and applied to simulate and forecast monthly sediment discharge
Considering the influence of dynamical factor of sediment discharge as well as the difficulty of calculating number of input cell, the model is provided with strong nonlinear mapping capacity and applied to simulate and forecast monthly sediment discharge
模型既能考虑到影响输沙量时间序列的动力因子，又能解决网络的输入单元数确定的困难和利用神经网络超强的非线性映射功能，通过对龙川江流域控制站月输沙量的拟合与预测表明其结果合理，预测精度较高。