CAJ | 학술논문

结合局部均值分解LMD（ Local mean decomposition ）算法和BP神经网络算法，提出一种全新的局部均值分解---BP神经网络位移时序预测模型。通过把实际监测的位移值作为训练样本，利用局部均值分解算法对其进行高度的自适应分解，得到多个生产函数PF（ Product function ）分量；而后通过BP神经网络模型对每一个PF分量进行预测，再把各个PF分量预测值进行重构累加，即可得到位移的预测值。通过BP神经网络对相关参数进行优化，达到了对于预测精度的改善。将该模型应用到永久船闸高边坡的三个监测点上进行位移时序预测中，结果表明，预测精度较高，具有一定的科学依据，在边坡体位移时序预测领域中具有极大的潜在价值。
결합국부균치분해LMD（ Local mean decomposition ）산법화BP신경망락산법，제출일충전신적국부균치분해---BP신경망락위이시서예측모형。통과파실제감측적위이치작위훈련양본，이용국부균치분해산법대기진행고도적자괄응분해，득도다개생산함수PF（ Product function ）분량；이후통과BP신경망락모형대매일개PF분량진행예측，재파각개PF분량예측치진행중구루가，즉가득도위이적예측치。통과BP신경망락대상관삼수진행우화，체도료대우예측정도적개선。장해모형응용도영구선갑고변파적삼개감측점상진행위이시서예측중，결과표명，예측정도교고，구유일정적과학의거，재변파체위이시서예측영역중구유겁대적잠재개치。
We present a novel LMD-BP neural network displacement time series prediction model in combination with the algorithms of local mean decomposition ( LMD ) and BP neural network .By selecting actual monitoring displacement data as the training sample and conducting highly adaptive decomposition on it using LMD algorithm , several product function (PF) components are obtained.After that, every PF component is predicted through BP neural network model , and then each prediction value is reconstructed and accumulated , the prediction value of displacement can be derived .BP neural network is used to optimise the correlated parameters , thus the improvement in prediction accuracy is reached .The model is put into application in displacement time series prediction carried out on three monitoring points at the high slope of permanent lock , result shows that the prediction accuracy is high , scientifically valid and has great potential value in the field of slope body displacement time series prediction .