粒子群-BP神经网络模型在大坝变形监测中的应用

Application of particle swarm optimization-BP neutral network in dam displacement prediction

针对BP神经网络的初始化权值和阈值的随机性,易导致训练速度慢和落入局部极小等弱点,本文运用具有并行特性和全局优化能力的粒子群算法(PSO)对BP神经网络的权值和阈值进行优化,建立了基于粒子群-BP神经网络的大坝变形监测模型,并以丰满大坝多年监测的坝顶水平位移资料为例进行实证分析。与经典BP神经网络模型的预测结果相比,粒子群-BP神经网络模型的收敛速度更快、预测精度更高。

Initialized weights and thresholds of the BP neural network are random,which results in slow convergence and easily converging to local optima.According to these characteristics,Particle Swarm Optimization（PSO）,which has a strong global searching ability,was utilized to optimize the weights and thresholds of the BP neural network in the paper.The model of dam displacement prediction based on PSO-BP neutral network was established and the transverse displacement monitoring data of Fengman Dam was used for evaluating the model.The experimental results showed that the PSO-BP neutral network model was faster in training and more accurate in prediction than the classic BP neural network mode.