| 摘要: |
| 针对传统静态前馈神经网络动态性能较差的缺点，提出一种基于遗传算法（GA）优化Elman神经网络连接权值的GA-Elman多元变量年径流预测模型.以新疆伊犁河雅马渡站径流预测为例进行实例分析，并构建传统Elman，传统BP和GA-BP多元变量年径流预测模型作为对比模型，预测结果与文献IEA-BP网络模型预测结果进行对比.结果表明：①GA-Elman模型的拟合及预测效果略优于文献IEA-BP模型，该模型用于多元变量年径流预测是合理可行的，具有较好的预测精度和泛化能力.②在相同网络结构及传递函数等条件下，GA-Elman模型的预测精度和泛化能力优于GA-BP模型，传统Elman模型优于传统BP模型，表明具有适应时变特性的Elman反馈动态递归网络预测性能优于BP网络；GA能有效优化Elman神经网络连接权值，使网络的预测精度和泛化能力有了较大提高. |
| 关键词: Elman神经网络 BP神经网络 遗传算法 径流预测 |
| DOI： |
| 分类号:P332 |
| 基金项目: |
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| An improved Elman neural network and its application to runoff forecast |
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CUI Dong-wen
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Wenshan Water Conservancy Bureau of Yunnan Province
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| Abstract: |
| In view of the disadvantage of the traditional static feedforward neural network′s poor dynamic performance， the GA-Elman multivariate annual runoff prediction model which is based on the GA optimization Elman neural network connection weights is developed in this study. The model is used in the runoff forecasting of Yamadu hydrometric station in the Ili River in Xinjiang， and the traditional Elman， BP and GA-BP traditional multivariate annual runoff prediction model are used as the contrast models. Their prediction results are compared with the results of the IEA-BP network model in one reference. Comparison of the results shows that： ① GA-Elman model′s fitting and forecasting effect is slightly better than that of the IEA-BP model， and GA-Elman model for multivariate annual runoff prediction is reasonable and feasible， which has better prediction accuracy and generalization ability； and ②with the same network structure and transfer function， the prediction accuracy and generalization ability of GA-Elman model are better than those of the traditional GA-BP model， and Elman model is superior to the traditional BP model. With the ability to adapt to the time-varying characteristics， Elman feedback dynamic recursive neural network prediction performance is better than BP network. GA can effectively optimize Elman neural network weights， so that the network prediction accuracy and generalization ability have been greatly improved. |
| Key words: Elman neural network BP neural network genetic algorithm runoff prediction |
