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改进最小二乘递推算法的洪水预报应用研究
引用本文:周轶,李致家. 改进最小二乘递推算法的洪水预报应用研究[J]. 水力发电, 2006, 32(8): 14-16
作者姓名:周轶  李致家
作者单位:河海大学水资源环境学院,江苏,南京,210098;河海大学水资源环境学院,江苏,南京,210098
摘    要:建立线性自回归模型,应用于洪水实时预报,并应用AIC、BIC这两种准则以确定自回归模型的阶数。最小二乘递推算法是估计自回归参数的一种常见方法。最小二乘法估算出的模型参数在预报误差平方和最小的条件下是最优解。研究中,为了强化时变系统的辩识以提高洪水预报精度,对数据采取衰减记忆、有限记忆及时变衰减记忆的方式,对基本的最小二乘递推算法提出了三种改进形式,并利用这几种改进算法进行了洪水演算,最后对几种算法的演算结果进行了比较。

关 键 词:线性自回归模型  最小二乘递推算法  有限记忆  自适应衰减因子  实时洪水预报
文章编号:0559-9342(2006)08-0014-03
收稿时间:2006-03-03
修稿时间:2006-03-03

Research on the Application of Three Improved RLS Procedures in Real-Time Flood Forecasting
Zhou Yi,Li Zhijia. Research on the Application of Three Improved RLS Procedures in Real-Time Flood Forecasting[J]. Water Power, 2006, 32(8): 14-16
Authors:Zhou Yi  Li Zhijia
Affiliation:The College of Water Resources and Environment, Hohai University, Nanjing Jiangsu 210098
Abstract:A linear AR model is set up to be applied in real-time flood forecasting. Two criterion AIC and BIC are used to decide the exponent number of the linear AR model. We often use RLS procedure to estimate the parameters of AP model. The estimated parameters using RLS procedure are the optimum solution based on the condition that the sum of square of the forecasted errors is the minimum. In research, three improved RLS procedures are developed to strengthen the characteristic-identification of the time-varying system in order to increase the accuracy of flood forecasting. The three procedures are operated separately. And the calculated results using the three procedures are compared.
Keywords:exponent number of linear AR model  faded-memory RLS procedure  fixed-memory RLS procedure  adaptive faded-memory  factor RLS procedure  real-time flood forecasting
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