遗传算法改进的BP神经网络对汛期三门峡水库泥沙冲淤量的计算

Calculation of sediment and scour of Sanmenxia reservoir in flood season by the BP neural network improved by GA

本文针对BP神经网络收敛速度慢和易于陷入极小值的问题,采用将遗传算法全局寻优和BP神经网络局部寻优相结合的方法,提高了BP神经网络的计算精度和收敛速度。应用该模型和一般BP神经网络模型对汛期三门峡水库的泥沙冲淤量进行了计算和预测。数值结果对比发现,经遗传算法改进后的BP神经网络模型在降低计算和预测的平均误差的同时,迭代次数比一般BP神经网络模型也大大减少。因此适用于求解如计算水库泥沙冲淤量等非线性问题。

In this paper, the problems of slow convergence speed and being prone to converge to minimum are solved by combining the characteristics of global optimization of GA （Genetic Algorithm） with local optimization of BP neural network, the calculation accuracy and convergence rate of BP neural network are improved. The sediment and scour of Sanmenxia reservoir in flood season are calculated and predicted by this model and BP neural network. Compared with normal BP neural network, the numerical results of BP neural network improved by GA show that the average errors of calculation and prediction are diminished and the iterative numbers are much less than that of the normal BP neural network. Thus the as calculation of sediment and scour BP neural network improved by GA is suitable to solve nonlinear problems such of reservoirs.