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.
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.
YUAN Jing, ZHANG Xiao-fengan. Real-time hydrological forecasting method of artificial neural network based on forgetting factor[J]. Advances in Water Science, 2004, 15(6): 787-792.
Citation: YUAN Jing, ZHANG Xiao-fengan. Real-time hydrological forecasting method of artificial neural network based on forgetting factor[J]. Advances in Water Science, 2004, 15(6): 787-792.

基于遗忘因子的BP神经网络水文实时预报方法

Real-time hydrological forecasting method of artificial neural network based on forgetting factor

  • 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。

     

    Abstract: Flood system is usually very complex,and always changes with different inflow from upstream,local rainfall,riverbed 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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