| Groundwater level prediction based on hybrid hierarchy genetic algorithm and RBF neural network |
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| 作者姓名： | 屈吉鸿 黄强 陈南祥 徐建新 |
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| 作者单位： | [1]School of Hydraulic and Electrical Engineering,Xi’an University of Technology,Xi’an 710048,China [2]North China University of Water Conversancy and Hydroelectric Power,Zhengzhou 450011,China |
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| 摘 要： | As the traditional non-linear systems generally based on gradient descent optimization method have some shortage in the field of groundwater level prediction, the paper, according to structure, algorithm and shortcoming of the conventional radial basis function neural network （RBF NN）, presented a new improved genetic algorithm （GA）： hybrid hierarchy genetic algorithm （HHGA）. In training RBF NN, the algorithm can automatically determine the structure and parameters of RBF based on the given sample data. Compared with the traditional groundwater level prediction model based on back propagation （BP） or RBF NN, the new prediction model based on HHGA and RBF NN can greatly increase the convergence speed and precision.
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| 关 键 词： | 混合分层遗传算法 RBF神经网络 地下水位 预测模型 |
Groundwater level prediction based on hybrid hierarchy genetic algorithm and RBF neural network |
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| QU Ji-hong, HUANG Qiang, CHEN Nan-xiang, XU Jian-xin.Groundwater level prediction based on hybrid hierarchy genetic algorithm and RBF neural network[J].Journal of Coal Science & Engineering(China),2007,13(2):170-174. |
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| Authors: | QU Ji-hong HUANG Qiang CHEN Nan-xiang XU Jian-xin |
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| Abstract: | |
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| Keywords: | hybrid hierarchy genetic algorithm radial basis function neural network groundwater level prediction model |
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