| 神经网络融合模型在大坝变形分析中的应用 |
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| 引用本文： | 张帆,胡伍生.神经网络融合模型在大坝变形分析中的应用[J].东南大学学报,2013(4):441-444. |
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| 作者姓名： | 张帆 胡伍生 |
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| 作者单位： | 东南大学交通学院,南京210096 |
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| 基金项目： | The Scientific Innovation Research of College Graduates in Jiangsu Province （ No. CXLX11_0143）. |
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| 摘 要： | 为了提高大坝变形分析模型的预测精度并检验模型的泛化能力，研究了大坝变形分析的BP神经网络模型，并基于神经网络BP算法和传统的统计模型建立了大坝变形分析的融合模型．结合陈村大坝多年的变形观测数据，对上述3种模型进行了试算及分析．分析结果表明，统计模型的平均预测精度为±0．477mm．BP神经网络模型的平均预测精度为±0．390mm，融合模型的平均预测精度为±0．318mm，相比统计模型和BP神经网络模型分别提高了33％和18％，且泛化能力较强，具有广泛的适用性．
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| 关 键 词： | 大坝变形分析 神经网络 统计模型 融合模型 |
Application of neural network merging model in dam deformation analysis |
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| Zhang Fan Hu Wusheng.Application of neural network merging model in dam deformation analysis[J].Journal of Southeast University(English Edition),2013(4):441-444. |
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| Authors: | Zhang Fan Hu Wusheng |
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| Institution: | Zhang Fan Hu Wusheng (School of Transportation, Southeast University, Nanjing 210096, China) |
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| Abstract: | In order to improve the prediction accuracy and test the generalization ability of the dam deformation analysis model, the back-propagation（BP） neural network model for dam deformation analysis is studied, and the merging model is built based on the neural network BP algorithm and the traditional statistical model. The three models mentioned above are calculated and analyzed according to the long-term deformation observation data in Chencun Dam. The analytical results show that the average prediction accuracies of the statistical model and the BP neural network model are ~ 0.477 and ＋- 0.390 mm, respectively, while the prediction accuracy of the merging model is ~0. 318 mm, which is improved by 33% and 18% compared to the other two models, respectively. And the merging model has a better generalization ability and broad applicability. |
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| Keywords: | dam deformation analysis neural network statistical model merging model |
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