粒子群-BP神经网络模型在大坝变形监测中的应用

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。

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

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.