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sparse parameter
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  相似匹配句对
     Integrated Sparse Model Selection and Parameter Identification with Applications
     模型的稀疏选择与参数辨识及应用
短句来源
     Parameter Estimation of GTD Model Based on Sparse Component Analysis
     基于稀疏成份分析的几何绕射模型参数估计
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     and b was empirical parameter.
     b为经验常数。
短句来源
     n—Parameter CAPM
     n个参数的资本性资产定价模型
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     FFT and Sparse Matrices
     FFT与稀疏矩阵
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  sparse parameter
The evidence is gathered in a very sparse parameter space, so that peak recovery is performed readily.
      
The first of these concerns the need to handle complex sparse parameter spaces, particularly with multi-scale parameters.
      


Objective The concept of the inverse autocorrelations was given,and its applications in indentifying ARMA models were displayed.Methods As to get a parsimonious ARMA model for a time series,some characteristics of the inverse autocorrelations were discussed in detail.Results As is shown in the given example,the inverse autocorrelations are of great value in identifying a desirable ARMA model,especially those with sparse parameters.Conclusion It's necessary to consider the inverse autocorrelations besides...

Objective The concept of the inverse autocorrelations was given,and its applications in indentifying ARMA models were displayed.Methods As to get a parsimonious ARMA model for a time series,some characteristics of the inverse autocorrelations were discussed in detail.Results As is shown in the given example,the inverse autocorrelations are of great value in identifying a desirable ARMA model,especially those with sparse parameters.Conclusion It's necessary to consider the inverse autocorrelations besides the autocorrelations and the partial auto correlations when identifying ARMA models.

目的 介绍逆自相关函数的意义 ,说明其在ARMA模型识别中的作用。方法 分析逆自相关函数以了解时序的结构特征 ,获得简约的拟合模型形式。结果 实例分析表明 ,逆自相关函数在疏系数模型的识别中 ,明确地补充刻划了模型结构 ,有可靠的应用价值。结论 在时间序列的ARMA模型识别中 ,考虑自相关函数与偏自相关函数的同时 ,充分利用逆自相关函数提供的信息 ,有助于选得较优模型。

The parametric analysis is presented for the sparse signal followed generalized Gaussian distribution (GGD). At first, the properties of GGD signal are discussed. A mathematical formula is established to compute the parameter of sparseness. It is shown that the parameter of Laplacian signal is 1, and that of Gaussian signal is 2. For a given GGD signal, comparing with Laplacian signal and Gaussian signal, we can intuitively know how sparse it is by calculating the sparse parameter. Two examples are given...

The parametric analysis is presented for the sparse signal followed generalized Gaussian distribution (GGD). At first, the properties of GGD signal are discussed. A mathematical formula is established to compute the parameter of sparseness. It is shown that the parameter of Laplacian signal is 1, and that of Gaussian signal is 2. For a given GGD signal, comparing with Laplacian signal and Gaussian signal, we can intuitively know how sparse it is by calculating the sparse parameter. Two examples are given to illustrate the fact that only when the source signals are sufficient sparse, we can (achieve) underdetermined blind source separation (BSS) by sparse representation.

对于服从广义高斯分布(Generalized Gaussian distribution,GGD)的稀疏信号进行了参数分析.首先给出了广义高斯分布信号的一些性质,通过对信号等高线的分析,导出了计算稀疏性参数的公式,通过该公式的计算可以得到,对Laplace信号稀疏性参数为1,对Gauss信号为2.参照Laplace信号和Gauss信号,对于给定的服从广义高斯分布的信号,通过稀疏度量的计算可以直观地知道它究竟多么稀疏.实例表明只有当信号充分稀疏时才能通过稀疏表示方法实现欠定盲源分离.

 
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