人民珠江
人民珠江
인민주강
PEARL RIVER
2014年
3期
46-48
,共3页
逐步回归%BP 神经网络%大坝变形分析
逐步迴歸%BP 神經網絡%大壩變形分析
축보회귀%BP 신경망락%대패변형분석
stepwise regression%BP neural network%dam deformation analysis
大坝变形预测的准确性对大坝的安全评估起着重要作用，而分析预报的方法至关重要。介绍了逐步回归模型和 BP 神经网络模型，提出将逐步回归模型和 BP 神经网络模型相结合的 SR -BP 混合模型，并通过实例证明了模型的可行性和有效性。与逐步回归模型和 BP 网络模型法相对比，计算量少、精度高、模拟效果较好。
大壩變形預測的準確性對大壩的安全評估起著重要作用，而分析預報的方法至關重要。介紹瞭逐步迴歸模型和 BP 神經網絡模型，提齣將逐步迴歸模型和 BP 神經網絡模型相結閤的 SR -BP 混閤模型，併通過實例證明瞭模型的可行性和有效性。與逐步迴歸模型和 BP 網絡模型法相對比，計算量少、精度高、模擬效果較好。
대패변형예측적준학성대대패적안전평고기착중요작용，이분석예보적방법지관중요。개소료축보회귀모형화 BP 신경망락모형，제출장축보회귀모형화 BP 신경망락모형상결합적 SR -BP 혼합모형，병통과실예증명료모형적가행성화유효성。여축보회귀모형화 BP 망락모형법상대비，계산량소、정도고、모의효과교호。
The accuracy of dam deformation prediction is important to the safety evaluation of dam,so the choice of analysis and predic-tion method in dam deformation is particularly important.In this paper,the stepwise regression model and neural network model are in-troduced ,and a hybrid model named SR -BP which is combined with stepwise regression model and BP neural network model are put forward ,and its feasibility and effectiveness are also demonstrated of by a case study.Compared with the stepwise regression model and BP neural network model total regression,the computation is simpler and has higher simulated precision and better forecasting effect.
