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least squares相关的网络例句

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与 least squares 相关的网络例句 [注:此内容来源于网络,仅供参考]

In this paper, we discuss the computational stability of two kinds of algorithms for solving Least-Squares problem, the normal equation method and the orthogonal decomposition methdd, and show that we can get better performance in Jater case in this aspect.

其一是基于正则方程的递推求解方法,这是目前各种自适应算法的基础;另一是正交分解算法。后者的计算稳定性要比前者高。研究了一种基本的正交分解自适应算法,并指出,如果初始条件恰当,它将得到和批处理同样的结果。

This algorithm is to use orthogonal polynomials to seek least-squares polynomial fitting.

详细说明:该算法是用正交多项式来求最小二乘拟合多项式。

To deal with data containing time drift, orthogonal polynomials fitting instead of least squares method is used.

研究了一种适用于这种过程的数据处理方法,利用权函数为1的特殊正交多项式做最佳平方逼近,从而避免求解病态方程组,得到较好的处理结果。

The multi point-fitting criterion model is built based on n-rank polynomials and orthogonal function least squares fitting criterion.

用n次多项式与基于正交函数的最小二乘拟合法,分别建立多点拟合模型。

The total least squares based on singular value decomposition is a method of solving an overdetermined system of linear equations that is appropriate when there are errors in both and .

摘 要:全最小二乘法是一种解决超定线性系统的方法,它基于奇异值分解,适用于当和都存在误差的时候。

Distinct from those methods, this paper's main idea is to reformulate CPs as an unconstrained minimization problem with the form of least squares of a system of overdetermined equations being equivalent to the CPs.

与这些方法不同,本文的基本思想是借助互补问题与某个超定方程组同解的事实,利用该方程组的最小二乘形式将原问题直接转化为无约束优化问题,这样不仅把问题转化为光滑优化问题,而且避免了引入光滑参数导致的不便。

Neumann, Successive overrelaxation methods for solving the rank deficient linear least squares problem, Linear Algebra Appl.

Miller and Neumann 提出SOR方法解决秩亏损线性最小二乘问题。

And use the least squares technique to paraboloid fitting the finite element node on the distorted antenna.

采用最小二乘法来对变形后的天线有限元节点进行抛物面拟合。

Firstly, a least squares fit of a paraboloid function to the Harris interest strength in the image grayscale surface is designed.

针对基于Harris原理提取的特征点的定位精度只能达到1个像素的问题,本文提出了一种基于Harris兴趣值的特征点子像素精确定位方法。

The quantitative analysis model of soluble solids content in tomato samples was built based on Partial least squares and its correlation coefficient between prediction and actual value is 0.954, yielding standard error of calibration=0.321, standard error of prediction=0.475%.

用偏最小二乘法建立的定量分析模型,其预测值和实测值的相关系数为0.954,校正标准差为0.321%,预测标准差为0.475%。

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