By apriori choosing regularization parameter, optimal convergence order of the regularized solution is obtained.
通过适当选取正则参数，证明了正则解具有最优的渐近收敛阶。
This paper utilizes L-curve method to determine the regularization parameter in the (above) two computation steps.
在两步计算中，均采用L曲线法来确定正则化参数α。
Choosing a small regularization parameter or shortening the inversion range properly can help improve the inversion quality.
合理选择小正则化参数或者缩小反演范围能改善反演质量。
Numerical results show that 'near optimal' parameter can be considered as an acceptable approximation of optimal regularization parameter with available priori information.
数值结果表明，在先验知识满足的条件下，近似最优参数法所找到的正则化参数是对最优正则化参数的较合理近似。
The principle of the method is introduced and some key points, i. e. the selection of the stabilization functional and the regularization parameter, are discussed in the paper.
文中阐述了方法的基本原理，并就稳定泛函和正则参数的选择等关键问题作了分析和论述。
So the mathematical regularization methods were proposed to solve this problem, which made use of regularization parameter to achieve a balance between the noise and the true solution.
为解决这一问题，数学上提出了利用正则化参数在真值和噪声之间寻求平衡的正则化求解思想。
In this thesis, we choose truncated singular value decomposition to solve the resulting matrix equations, while the regularization parameter of TSVD is determined by the L-curve criterion.
鉴于此，必须采用正则化方法，本文中选用的是截断奇异值分解，其正则化参数用l -曲线准则来确定。
In this thesis, we choose truncated singular value decomposition to solve the resulting matrix equations, while the regularization parameter of TSVD is determined by the L-curve criterion.
鉴于此，必须采用正则化方法，本文中选用的是截断奇异值分解，其正则化参数用l -曲线准则来确定。
应用推荐