| 基于主成分与广义回归神经网络耦合的寒区水库裂缝开合度预测模型 |
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| 引用本文： | 古力米热·哈那提,美丽古丽·买买提,孟波.基于主成分与广义回归神经网络耦合的寒区水库裂缝开合度预测模型[J].干旱区地理,2011(4):584-590. |
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| 作者姓名： | 古力米热·哈那提 美丽古丽·买买提 孟波 |
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| 作者单位： | 新疆水利水电科学研究院大坝安全监测中心; |
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| 基金项目： | 国家自然科学基金项目(41071026); 国家重点基础研究发展计划973项目(2009CB421302)资助 |
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| 摘 要： | 水库裂缝开合情况对于水库的安全运行极为重要.将主成分分析法与广义回归神经网络结合在一起,进行水库裂缝开合度的预测.结果表明:应用主成分分析与广义回归神经网络相耦合的模型可以很好的反映环境因子(水压力因子、温度因子、时效因子)与水库裂缝开合度之间的非线性函数映射关系.同时利用Matlab软件对新疆某寒区水库裂缝的开合度进...
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| 关 键 词： | 主成分分析 广义回归神经网络 水库裂缝开合度 预测模型 |
Prediction modeling for opening-closing degrees of reservoir cracks base on the GRNN in cold area |
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| Gulimire HANATI,Meiliguli MAIMAITI,MENG Bo.Prediction modeling for opening-closing degrees of reservoir cracks base on the GRNN in cold area[J].Arid Land Geography,2011(4):584-590. |
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| Authors: | Gulimire HANATI Meiliguli MAIMAITI MENG Bo |
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| Institution: | Gulimire HANATI,Meiliguli MAIMAITI,MENG Bo (Xinjiang Institute of Water Resources and Hydroelectric Sciences,Urimqi 830049,Xinjiang,China) |
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| Abstract: | Opening and closing of reservoir cracks plays an important role to the safety of reservoir.Although the principal component analysis can effectively deal with the problems of multi-collinearity and non-linearity among variables.The method of neural network is an ideal tool to deal with the problem of non-linearity,but serious correlation of input data will make the network unsteady.An attempt has been made to investigate the possibility of using generalized regression neural network to predict the opening-c... |
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| Keywords: | principal component analysis GRNN opening-closing degrees of reservoir cracks prediction model |
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