Application of BFGS-BP in Tunnel Deformation Monitoring Data Processing

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...

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Bibliographic Details
Published in2011 International Conference on Internet Computing and Information Services pp. 411 - 414
Main Authors Wang Zegen, Gao Yuyun, Hu Guangqiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2011
Subjects
BFGS algorithm
BP neural network
Data models
Data processing
Deformable models
deformation monitoring
Monitoring
Prediction algorithms
Predictive models
Training
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Summary: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.
ISBN:1457715619
9781457715617
DOI:10.1109/ICICIS.2011.107