BP神经网络算法考虑激活函数后对强震路基塌陷变形预测研究

Use of a BP Neural Network Algorithm with Activation Functionto Predict the Subgrade Subsidence and DeformationInduced by Strong Earthquakes

摘要:

当前强震后铁路路基变形预测中，相关算法未能考虑激活函数的非线性属性，造成非线性变形特征数据提取不完整，且其特征数据存在偏差，陷入局部最优解。文章提出BP神经网络算法考虑激活函数后对强震路基塌陷变形预测方法，采用双极S因子补偿ReLU非线性激活函数，优化BP神经网络算法，解决非线性路基变形特征数提取问题。利用数据标准化归一方法，对已修正提取的全部变形特征数据进行偏差数据归一，得到路基变形特征数据集合，结合强震后路基变形连接权值计算路基变形预测值，完成强震路基塌陷变形预测。结合实测结果，在matlab下进行预测实验，结果表明所提混合方法可以有效地对水平地震作用下铁路路基塌陷变形程度进行预测，且预测值在误差允许范围内，为铁路的安全运行以及实时维护提供重要依据。

Abstract:

In the current mode of railway subgrade deformation prediction after strong earthquakes, the algorithm fails to consider the nonlinear properties of the activation function, resulting in incomplete extraction of nonlinear deformation features and errors in the characteristic data. Based on this, the back-propagation (BP) neural network algorithm, incorporating the activation function, was proposed to predict the subgrade subsidence deformation after strong earthquakes. The nonlinear activation function of ReLU was compensated for by bipolar S factor, so as to optimize the BP neural network algorithm and solve the problem of characteristic number extraction in nonlinear subgrade deformations. Using the data standardized normalization method, the deviation data of all the characteristic data extracted and corrected was normalized to obtain the subgrade deformation characteristic data set. The subgrade subsidence deformation after a strong earthquake could then be predicted. Combined with the measured results, the prediction experiment was carried out using Matlab. The results showed that the proposed hybrid method can effectively predict the deformation degree of railway subgrade subsidence under horizontal earthquake action, and the error of predicted value is within acceptable limits.