CAJ | 학술논문

在使用传统BP神经网络算法建模进行预测过程中，由于初始权值和阈值是随机给定的，易使网络陷入局部最优，从而导致预测精度较低。利用具有较强优化能力的粒子群算法（ particle swarm optimization ，PSO）优化BP神经网络在训练过程中的初始权值和阈值，建立新的预测模型，以青岛地铁3号线保河区间隧道监测数据为例进行验证分析，研究结果表明，与传统BP神经网络预测算法相比，使用PSO算法优化的BP神经网络预测算法可以得到更优的预测结果。
재사용전통BP신경망락산법건모진행예측과정중，유우초시권치화역치시수궤급정적，역사망락함입국부최우，종이도치예측정도교저。이용구유교강우화능력적입자군산법（ particle swarm optimization ，PSO）우화BP신경망락재훈련과정중적초시권치화역치，건립신적예측모형，이청도지철3호선보하구간수도감측수거위례진행험증분석，연구결과표명，여전통BP신경망락예측산법상비，사용PSO산법우화적BP신경망락예측산법가이득도경우적예측결과。
When we build models to predict using the traditional BP neural network algorithm , because the initial weights and thresh-olds are stochastic , it is easy to make the network into local optimum , finally resulting in a lower prediction accuracy .Use particle swarm optimization algorithm to optimize initial weights and thresholds of BP neural network in the training process , then establish new forecasting model , applying to Qingdao monitoring data of Metro Line 3 Paul River tunnel as example to analyze , and the results show that, compared with the traditional BP neural network prediction algorithm , using PSO algorithm to optimize BP neural network can get a better predictions .