| 基于遗忘因子的BP神经网络水文实时预报方法 |
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| 引用本文： | 袁晶,张小峰.基于遗忘因子的BP神经网络水文实时预报方法[J].水科学进展,2004,15(6):787-792. |
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| 作者姓名： | 袁晶 张小峰 |
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| 作者单位： | 武汉大学水资源与水电工程科学国家重点实验室, 湖北, 武汉, 430072 |
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| 基金项目： | 国家自然科学基金资助项目(50279035)，国家高技术研究发展计划(863)资助项目，世界银行资助ANFAS项目~~ |
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| 摘 要： | 在应用神经网络进行洪水预报时,因洪水系统随着河道上游来流、区间降雨、河床演变等因素的动态变化,其特性并不总是按照基本相同的规律变化,对这类系统的参数辨识,要求算法具有较强的实时跟踪能力,以适应模拟或预测洪水运动变化过程的要求。在BP神经网络模型的基础上,运用最小二乘递推算法,引入时变遗忘因子实时跟踪模型中时变参数的变化,建立了神经网络在非线性系统中动态系统输入、输出数据间的映射关系。计算实例表明:该法对参数的快速时变具有较快的跟踪能力和较高的辨识精度,是一种非常实用的水文实时预报方法。
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| 关 键 词： | 神经网络 最小二乘递推算法 时变遗忘因子 时变参数 水文实时预报 |
| 文章编号： | 1001-6791(2004)06-0787-06 |
| 收稿时间： | 2003-08-12 |
| 修稿时间： | 2003年8月12日 |
Real-time hydrological forecasting method of artificial neural network based on forgetting factor |
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| YUAN Jing,ZHANG Xiao-fengan University,Wuhan ,China.Real-time hydrological forecasting method of artificial neural network based on forgetting factor[J].Advances in Water Science,2004,15(6):787-792. |
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| Authors: | YUAN Jing ZHANG Xiao-fengan University Wuhan China |
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| Affiliation: | State Key Labor atory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China |
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| Abstract: | Flood system is usually very complex, and always changes with different inflow from upstream, local rainfall, river-bed deformation and other factors. When the back propagation (BP) neural network is applied in such system for flood forecasting, the algorithm must have ability for real-time tracing of the changes of parameters in the system. In this paper, a variable weighted forgetting factor based on recursive least-squares parameter estimation is introduced into the BP model to simulate such time-variant system. Each weight of the neural network can be real-time modified and the transitional invariable mapping relationship between input and output in the non-liner system of neural network is improved. And two examples are given to demonstrate the effectiveness of the improvement.The calculated result shows that the time-variable weights can be traced with a fast speed and agrees well with the measured data. |
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| Keywords: | artificial neural network recursive least-squares time dependent forgetting factor time variant parameter real-time hydrological forecasting |
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