Application of BFGS-BP in Tunnel Deformation Monitoring Data Processing

Wang Zegen, Gao Yu-yun, Hu Guangqiang
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Abstract

In order to overcome the disadvantages such as low calculation precision and convergence rate of traditional BP neural network algorithm, a kind of nonlinear optimization method-BFGS method for unconstrained extreme problem is introduced into BP neural network algorithm, and a BFGS-BP neural network model is developed, which is applied well in tunnel deformation monitoring data processing and forecasting with uncertainty and nonlinearity. With the example of the observation data of vault crown settlement of some tunnel construction process, the test of training and forecast experiments of BFGS - BP were developed. The result shows that BFGS-BP model has higher calculation precision and convergence rate than the traditional one.
BFGS-BP在隧道变形监测数据处理中的应用
针对传统BP神经网络算法计算精度低、收敛速度慢等缺点,在BP神经网络算法中引入一种求解无约束极值问题的非线性优化方法——bfgs方法,建立了BFGS-BP神经网络模型,该模型在具有不确定性和非线性的隧道变形监测数据处理和预测中具有良好的应用前景。以某隧道施工过程拱顶沉降观测数据为例,开展了BFGS - BP的训练和预报试验。结果表明,与传统模型相比,BFGS-BP模型具有更高的计算精度和收敛速度。
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