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prediction-methods-for-hydrology

于 2014-04-12 发布 文件大小:5KB
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代码说明:

  径流预报常用的几种模型:AR模型,BP模型,RBF模型,GM(1,N)模型;预报数据预处理方法:自相关函数以及偏自相关函数确定法;EMD方法(Several commonly used runoff forecasting model: AR model, BP model, RBF model, GM (1, N) model forecast data preprocessing methods: autocorrelation function and partial autocorrelation function to determine the law EMD Method)

文件列表:

prediction methods for hydrology
................................\ACF_and_PACF.m,1572,2013-08-19
................................\AR.m,1370,2013-12-05
................................\BP.m,744,2013-12-06
................................\emd.m,1390,2013-09-08
................................\GM(1,N).m,1663,2013-12-05
................................\RBF.m,1269,2013-12-05

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    迭代自适应Simpson,Lobatto积分 In almost every standard book on numerics quadrature algorithms like the adaptive Simpson or the adaptive Lobatto algorithm are presented in a recursive way. The benefit of the recursive programming is the compact and clear representation. However, recursive quadrature algorithms might be transformed into iterative quadrature algorithms without major modifications in the structure of the algorithm. We present iterative adaptive quadrature algorithm (adaptiveSimpson and adaptiveLobatto), which preserves the compactness and the clarity of the recursive algorithms (e.g. quad, quadv, and quadl). Our iterative algorithm provides a parallel calculation of the integration function, which leads to tremendous gain in run-time, in general. Our results suggest a general iterative and not a recursive implementation of adaptive quadrature formulas, once the programming language permits parallel access to the integration function. For details the attached PDF file Conrad_08.pdf. (Iterative Adaptive Simpson, Lobatto Points In almost every standard book on numerics quadrature algorithms like the adaptive Simpson or the adaptive Lobatto algorithm are presented in a recursive way. The benefit of the recursive programming is the compact and clear representation. However, recursive quadrature algorithms might be transformed into iterative quadrature algorithms without major modifications in the structure of the algorithm.We present iterative adaptive quadrature algorithm (adaptiveSimpson and adaptiveLobatto), which preserves the compactness and the clarity of the recursive algorithms (eg quad, quadv, and quadl) . Our iterative algorithm provides a parallel calculation of the integration function, which leads to tremendous gain in run-time, in general. Our results suggest a general iterative and not a recursive implementation of adaptive quadrature formulas, once the programming language permits parallel access to the integration function. For details the attached PDF file Conrad_08.pdf.)
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