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parameter regularization
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  “parameter regularization”译为未确定词的双语例句
     According to this method, by introducing the parameter regularization, the convergence speed of attributed scattering center extraction is accelerated. The accuracy and efficiency of parameter estimation are improved.
     在该方法中,通过引入参数规则化处理步骤,解决了属性散射中心特征提取方法的收敛问题,提高了属性散射中心特征参数估计的精度和效率;
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  相似匹配句对
     Selection of Regularization Parameter for Anisotropic Diffusion Filtering
     各向异性扩散滤波的正则化参数选取方法
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     A New Method for Selection of Regularization Parameter
     一种获取正则化参数的新方法
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     Scattering Parameter
     散射参数
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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     Robustness of Regularization Methods
     正则化方法的强健性
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  parameter regularization
Multi-parameter regularization techniques for ill-conditioned linear systems
      
Multiple Parameter Regularization: Numerical Solutions and Applications to the Determination of Geopotential from Precise Satell
      
To obtain stable approximate solutions of the estimation problem, we use a multi-parameter regularization approach, where a least-squares fitting of empirical densities is superposed by a quadratic penalty term of fitted semi-invariants with weights.
      
A multi-parameter regularization approach for estimating parameters in jump diffusion processes
      
In Section IV, we propose the multi-parameter regularization methods via the bi-orthogonal wavelets and via the tight-frame systems.
      
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In this paper the authors present a kind of two-parameter regularization method for solving nonlinear ill-posed operator equations, and the existence, stability, convergence of the solution of this method is discussed.

对非线性不适定算子方程 ,引入一种双参数正则化方法求解 ,讨论了这种正则化方法解的存在性、稳定性和收敛性 .

Scattering center is an important feature for SAR imagery target recognition. Based on attributed scattering center model, on the basis of reference [6], a modified method of extracting SAR target scattering center features from imagery domain is presented. According to this method, by introducing the parameter regularization, the convergence speed of attributed scattering center extraction is accelerated. The accuracy and efficiency of parameter estimation are improved. Also a method that can determine...

Scattering center is an important feature for SAR imagery target recognition. Based on attributed scattering center model, on the basis of reference [6], a modified method of extracting SAR target scattering center features from imagery domain is presented. According to this method, by introducing the parameter regularization, the convergence speed of attributed scattering center extraction is accelerated. The accuracy and efficiency of parameter estimation are improved. Also a method that can determine the number of scattering centers and distinguish their structure simultaneously is proposed, which realizes the reliable determination of the structure of scattering center. Finally the modified method is verified by the experimental results of simulated real MSTAR SAR imagery data.

散射中心是SAR图像目标识别的重要特征。本文基于属性散射中心模型,在文献[6]的基础上,提出了一种改进的图像域SAR目标散射中心特征提取方法。在该方法中,通过引入参数规则化处理步骤,解决了属性散射中心特征提取方法的收敛问题,提高了属性散射中心特征参数估计的精度和效率;提出了一种能同时实现散射中心数目确定和结构判别的方法,实现了散射中心类型的可靠判别。仿真数据和MSTAR实测SAR图像数据的实验结果,验证了本文改进的图像域SAR目标散射中心特征提取方法的有效性。

>=The analysis on the output auto-covariance matrix of array and its eigenvector decomposition plays an important role in array techniques, both for algorithm optimization and parameter regularization. In this paper, with definition of a relative convergence factor between the resulted eigenvectors, the difference between the sampled auto-covariance matrix and the ideal one is measured. Thus, the different impacts on the relative convergence factor from several factors, such as the correlation coefficient...

>=The analysis on the output auto-covariance matrix of array and its eigenvector decomposition plays an important role in array techniques, both for algorithm optimization and parameter regularization. In this paper, with definition of a relative convergence factor between the resulted eigenvectors, the difference between the sampled auto-covariance matrix and the ideal one is measured. Thus, the different impacts on the relative convergence factor from several factors, such as the correlation coefficient between signals, orthogonalities between signals and between signals and noises, and the snapshots etc., is evaluated numerically in a specified scenario, which is expected to be helpful in effective array design.

本文从分析阵列天线输出的理想自相关矩阵及其特征结构出发,通过引入相对逼近因子的定义,讨论了实际阵列天线输出样本的自相关矩阵与连续采样情形下自相关矩阵的差别,及其与采样快拍数、信号的相关性、信号与噪声之间的正交性以及噪声的正交性等因素的关系。通过数值计算,具体分析了不同情形下,样本自相关矩阵收敛于理想自相关矩阵的速度变化。

 
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