Deformation Monitoring Model of Jinping-I Hydropower Station Based on NACA-BP Neural Network

De Wen Cai; Chen Fei Shao; Di Kai Wang; Er Feng Zhao; Meng Yang

doi:10.4028/www.scientific.net/AMM.704.257

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 704Deformation Monitoring Model of Jinping-I...

Deformation Monitoring Model of Jinping-I Hydropower Station Based on NACA-BP Neural Network

Abstract:

Back Propagation (BP) neural network can learn and store a large number of input-output model nonlinear relationships with simple structure. Niche ant colony algorithm (NACA) combines the ant colony algorithm (ACA) with the niche technology in order to add its local search ability to ACA with preserving the intelligent search ability and robustness of ACA. To optimize predicting model establishment of the dam monitoring data, NACA and BP neural network modeling method are combined to establish a prediction model of horizontal displacement monitoring data. The traditional BP neural network prediction model is established to make a comparison with the NACA. The results show that NACA-BP neural network method can speed up the convergence rate of BP neural network and enhance local search ability and prediction accuracy.

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Periodical:

Applied Mechanics and Materials (Volume 704)

Pages:

257-260

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.704.257

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Online since:

December 2014

Authors:

De Wen Cai, Chen Fei Shao*, Di Kai Wang, Er Feng Zhao, Meng Yang

Keywords:

Ant Colony Algorithm, BP Neural Network, Dam Safety Monitoring, Niche Technology, Prediction Model

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* - Corresponding Author

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