同时给出一种自适应确定正则化参数的方法。
At the same time, the method to choose regularization parameter adaptively is given.
在两步计算中，均采用L曲线法来确定正则化参数α。
This paper utilizes L-curve method to determine the regularization parameter in the (above) two computation steps.
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。
Choosing a small regularization parameter or shortening the inversion range properly can help improve the inversion quality.
两者的有机结合可以辨证地处理正则化参数和算子的选择以及先验模型的分布计算问题。
We could dialectically give the processing about choices of regularity coefficients, operators and the calculation of distributed prior-models accor…
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。
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.
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。
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.
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.
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。
Numerical results show that 'near optimal' parameter can be considered as an acceptable approximation of optimal regularization parameter with available priori information.
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。
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.
将GMRES和不同的正则化参数选取准则相结合—外层应用已知误差水平的后验选取、内层应用未知误差水平准则，提出一类双层正则化gmres方法。
Combining a posterior rules with error-known in outer regularization and error-free rules in inner regularization with GMRES, a kind of double regularized GMRES methods are proposed.
运用经典的哈密顿正则方程，建立了冲击式压实机的参数化动力学模型。
The parameterized dynamic model of the impact compactor is made based on the classical Hamilton's canonical equations.
本文是在MPI网络并行环境中，将求解二维热物性方程参数的反问题用正则化方法结合并行遗传算法进行数值求解。
This thesis focuses on determination of parameters in a two-dimension heat conduction equation on the MPI network parallel environment, by solving an inverse problem using the regularization method.
正则性是参数曲线曲面的重要代数性质，是由参数曲线曲面的参数化决定的。
Regularity is an important algebraic property of parametric curve and surface, which depends on the parameterization of parametric curve and surface.
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。
Chapter 3 studies and analyzes the wavelet-based image restoration algorithms, including threshold deconvolution algorithm, iterative regularization algorithm and parameter model algorithm.
本文根据常微分方程参数反问题的数学理论，将正交化方法同有限差分法结合用于确定水质模型参数，并与正则化方法、最速下降法和共轭梯度法作了比较。
The comparison of the calculation results show that orthogonal rule method is fast, simple and reliable, and is applicable to the calculation of the water quality modeling parameters.
基于半参数模型的GPS系统误差处理的关键之一是选择合适的正则化矩阵。
One of the crucial steps is choosing an appropriate regularizer in processing GPS systematic errors based on the semi-parametric model.
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。
Considering the regularization effect of homotopy parameter, we adopt a two-step update scheme of homotopy parameter.
利用核十六极形变最普遍的参数化形式，通过正则量子化程序，导出了参数空间十六极振动的量子化哈密顿量，分析了十六极形变和振动对于核结构研究的重要意义．。
The quantized Hamiltonian is derived from the general parametral form of the hexadecapole Vibration in the space of parameter, which maybe important to analyze properties of nuclear stature.
根据纹理特征的局部马尔可夫性和高斯变量的条件回归之间的关系，将复杂的模型选择转变为较简单的变量选择，应用惩罚正则化技巧同步选择邻域和估计参数。
The structure of the GGM is explored by the connection between the local Markov property of texture features and the conditional regression of Gaussian random variables.
根据纹理特征的局部马尔可夫性和高斯变量的条件回归之间的关系，将复杂的模型选择转变为较简单的变量选择，应用惩罚正则化技巧同步选择邻域和估计参数。
The structure of the GGM is explored by the connection between the local Markov property of texture features and the conditional regression of Gaussian random variables.
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