一种改进AFSA-Elman边坡位移预测网络

Elman网络在边坡位移序列预测的应用中，对于网络隐含层神经元个数、阈值的选取没有具体的定论，且收敛速度慢，容易陷入局部解.基于此，将人工鱼群算法与 Elman 网络相结合，建立了改进的 AFSA-Elman 边坡位移预测网络，修正鱼群算法的步长，并利用经改进后鱼群算法强大的寻优能力，对Elman网络的初始权值和阈值进行优化，提高了Elman网络的预测精度和收敛速度.将改进的AFSA-Elman网络与传统Elman网络以及AFSA-BP网络进行对比，并模拟了3种网络的迭代过程，发现改进的AFSA-Elman预测网络较以上两种预测网络具有较高的精度，收敛性更好，更适用于边坡位移的预测.

An Improved AFSA-Elman Slope Displacement Prediction Network

In the prediction of slope displacement sequence， there is no specific conclusions on the number of neurons and thresholds in the Elman network hidden layer. The convergence speed is slow， and it is easy to fall into the local solution. Based on this， the improved AFSA-Elman slope displacement prediction network was established by combining the artificial fish swarm algorithm with the Elman network. In order to improve the prediction accuracy and convergence speed of Elman network， the step size of artificial fish swarm algorithm was modified and the initial weights and thresholds of Elman network were optimized by using the powerful optimization ability of the improved fish swarm algorithm. The improved AFSA-Elman network was compared with the traditional Elman network and AFSA-BP network， and the iterative process of the three networks was simulated. The result found that the improved AFSA-Elman prediction network has higher precision and convergence speed than those of the above two prediction network， and it is more suitable for the prediction of slope displacement.