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Groundwater level prediction based on hybrid hierarchy genetic algorithm and RBF neural network 被引量:1
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作者 屈吉鸿 黄强 +1 位作者 陈南祥 徐建新 《Journal of Coal Science & Engineering(China)》 2007年第2期170-174,共5页
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 shortcomi... 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. 展开更多
关键词 hybrid hierarchy genetic algorithm radial basis function neural network groundwater level prediction model
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