| 小波神经网络组合时序模型在径流预报中应用 |
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| 引用本文： | 兰盈盈.小波神经网络组合时序模型在径流预报中应用[J].南昌水专学报,2014(3):10-13. |
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| 作者姓名： | 兰盈盈 |
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| 作者单位： | 南昌工程学院水利与生态工程学院,江西南昌330099 |
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| 基金项目： | 江西省教育厅青年科学基金项目（GJJ11254） |
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| 摘 要： | 构建小波与人工神经网络组合时序模型,该模型利用morlet小波基函数取代人工神经网络的激发函数,通过平移因子和伸缩因子确定小波基函数,采用误差逆向传播算法训练网络和预测.利用此模型对赣江河段上外洲水文站月径流变化趋势进行预测,并将其计算结果与BP神经网络进行比较,结果表明利用小波神经网络进行时间序列预测效果较理想.
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| 关 键 词： | 小波分析 人工神经网络 时间序列 径流预测 |
Application of wavelet neural network combined timing model in runoff prediction |
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| LAN Yingying.Application of wavelet neural network combined timing model in runoff prediction[J].Journal of Nanchang College of Water Conservancy and Hydroelectric Power,2014(3):10-13. |
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| Authors: | LAN Yingying |
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| Affiliation: | LAN Yingying (School of Hydraulic and Ecological Engineering,Nanchang Institute of Technology ,Nanchang 330099, China) |
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| Abstract: | In this paper,wavelet analysis and artificial neural networks are combined into a timing model.The model uses morlet wavelet function to replace artificial neural network excitation function and backward error propagation algorithm to train the network and prediction. It is used to predict the annual runoff characteristics of Waizhou hydrological station in Ganjiang River. The result shows that compared with BP artificial neural networks,artificial neural networks prove to be a more effective way. |
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| Keywords: | wavelet analysis artificial neural networks time series runoff prediction |
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