At the same time, the method to choose regularization parameter adaptively is given.
同时给出一种自适应确定正则化参数的方法。
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 the end, the genetic algorithms which has better precision and efficiency is adopted for finding the optimal regularization parameter based on the solution rule of regularization parameter.
最后根据正则化参数的确定原则，采用精度高和适应性更好的遗传算法确定最优正则化参数。
Secondly, to perfect the known restoring models, a new space-adaptive regularization model of image restoration is constructed by redesigning regularized parameter and regularized item.
第二，在现有复原模型的完善上，重新构建正则化参数与正则化项，构造了新的具有空间自适应性质的正则化图像复原模型。
Chapter 3 studies and analyzes the wavelet-based image restoration algorithms, including threshold deconvolution algorithm, iterative regularization algorithm and parameter model algorithm.
第3章研究和分析了基于小波的图像复原算法，包括小波域的阈值反卷积算法、迭代正则化算法和参数模型算法。
Considering the regularization effect of homotopy parameter, we adopt a two-step update scheme of homotopy parameter.
同时在分析了同伦参数正则化效应的基础上，提出一种两段同伦参数修正方法。
It is shown that the method can achieve an optimal value of the parameter in the whole range, and therefore provides an efficient way for obtaining an optimal regularization solution.
并对一桁架的荷载分布进行了重构，结果表明，这一方法是寻求最优正则解的一条有效途经。
It is shown that the method can achieve an optimal value of the parameter in the whole range, and therefore provides an efficient way for obtaining an optimal regularization solution.
并对一桁架的荷载分布进行了重构，结果表明，这一方法是寻求最优正则解的一条有效途经。
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