交通科技
交通科技
교통과기
TRANSPORTATION SCIENCE & TECHNOLOGY
2014年
6期
73-75
,共3页
广义回归神经网络%BP 神经网络%路基%沉降预测
廣義迴歸神經網絡%BP 神經網絡%路基%沉降預測
엄의회귀신경망락%BP 신경망락%로기%침강예측
generalized regression neural network%BP neural network%subgrade%settlement predic-tion
为了对路基沉降变化规律进行预测，避免发生工程事故，提出了将广义回归神经网络模型应用于软土地基沉降预测中的方案。通过广义回归神经网络的基本理论和概念，采用实际工程数据，用 BP 神经网络方法和广义回归神经网络方法进行了预测分析，比较了2种方法的3组预测结果。工程实例预测结果表明，广义回归神经网络方法的均方误差和决定系数表现都优于 BP 神经网络方法；证明该方法是可行且有效的。
為瞭對路基沉降變化規律進行預測，避免髮生工程事故，提齣瞭將廣義迴歸神經網絡模型應用于軟土地基沉降預測中的方案。通過廣義迴歸神經網絡的基本理論和概唸，採用實際工程數據，用 BP 神經網絡方法和廣義迴歸神經網絡方法進行瞭預測分析，比較瞭2種方法的3組預測結果。工程實例預測結果錶明，廣義迴歸神經網絡方法的均方誤差和決定繫數錶現都優于 BP 神經網絡方法；證明該方法是可行且有效的。
위료대로기침강변화규률진행예측，피면발생공정사고，제출료장엄의회귀신경망락모형응용우연토지기침강예측중적방안。통과엄의회귀신경망락적기본이론화개념，채용실제공정수거，용 BP 신경망락방법화엄의회귀신경망락방법진행료예측분석，비교료2충방법적3조예측결과。공정실례예측결과표명，엄의회귀신경망락방법적균방오차화결정계수표현도우우 BP 신경망락방법；증명해방법시가행차유효적。
In order to predict the variation of subgrade settlement,to avoid projects accidents,general-ized regression neural network model in subgrade settlement prediction program is proposed.Firstly, the basic theories and concepts of generalized regression neural network are discussed;Secondly, based on the actual project data,generalized regression neural network method and BP neural network model are applied for prediction and analysis;Finally,three groups based on two prediction methods are compared.Projects prediction results show that mean square error and the coefficient of determi-nation performance of generalized regression neural network model are better than BP neural network's;and the results prove that the method is feasible and effective.
