1) M-least-squares solution
M-最小二乘解
2) least squares solution
最小二乘解
1.
An iterative method for the least squares solutions of a pair of matrix equations and its optimal approximation;
矩阵方程组的最小二乘解及其最佳逼近的迭代算法
2.
Acquires the least squares solutions of the matrix equation AXB=E,CXD=F by constructing the normal equation of the matrix equation and applying the generalized singular-value decomposition of coefficient matrices.
借助于矩阵方程AXB=E,CXD=F的正规方程及系数矩阵的广义奇异值分解,得到了此矩阵方程的最小二乘解。
3.
The least squares solution of inverse problems of generalized skew symmetric matrices and It s optimal approximation problems are discussed.
讨论了线性流形上广义次对称矩阵反问题的最小二乘解及其逼近问题 。
3) least square solution
最小二乘解
1.
The conclusion that the force Jacobian calibration is to solve least square solution of overdetermined linear equations.
建立了并联天平的静态标定数学模型,得出力雅可比矩阵标定的实质是求解超定线性方程组最小二乘解的结论。
2.
In this paper,for a right system of linear equations Ax=b over the quaternion field,we discuss, under the condition of a positive definite weight,the solutions with minimal N-norms, the M-least square solutions and in particular, the M-least square solutions with minimal N-norms.
讨论了四元数体上右线性方程组的加正定权的极小范数解、最小二乘解和极小范数最小二乘解。
3.
Then by using the properties of the generalization reflexive (antireflexive) matrices the problems are discussed that least square solutions of systems of linear equation AX=b, matrices equation AX=B and AXB=C.
在四元数体Ω上引入了自反向量、自反矩阵和广义自反矩阵等概念,利用广义自反矩阵和广义反自反矩阵的性质讨论了线性方程组AX =b、矩阵方程AX =B及AXB =C的最小二乘解问题:当A为广义自反矩阵或广义反自反矩阵时,可将线性方程组AX =b的最小二乘解问题化为两个较小独立的子问题去讨论;当A、B都是广义自反矩阵或广义反自反矩阵时,可将矩阵方程AX =B的最小二乘解问题化为线性方程组的最小二乘解问题去讨论。
4) least-square solution
最小二乘解
1.
The least-square solution of the matrix equation A~TXA=D in anti-symmetric and persymmetric matrix;
矩阵方程A~TXA=D的反对称次对称最小二乘解
2.
The least-square solution of matrix equation A~TXB-B~TX~TA=D;
矩阵方程A~TXB-B~TX~TA=D 的最小二乘解
3.
Least-Square Solution to the Inverse Problem of Generalized Centro-anti-symmetric Matrices;
广义中心反对称矩阵反问题的最小二乘解
5) least-square solutions
最小二乘解
1.
By applying the generalized singular value decomposition,the expression of the weighted least-square solutions of a matrix equation is provided.
利用矩阵的广义奇异值分解,得到了一类矩阵方程的加权最小二乘解的一般表达式,以及能够对给定矩阵进行最佳逼近的解矩阵。
2.
Given matrix X,Y and B, the anti-centrosymmetric least-square solutions A of inverse problem YAX = B are considered.
给定矩阵Y,X和B,得到了矩阵方程YAX=B的反中心对称最小二乘解。
6) least-squares solution
最小二乘解
1.
The least-squares solutions of inverse problems for generalized(R,S)-symmetric matrices;
广义(R,S)-对称矩阵反问题的最小二乘解
2.
The least-squares solutions of inverse problems for anti-skew-symmetric on the linear manifold;
线性流形上反次对称矩阵反问题的最小二乘解
3.
Least-squares Solution of the Matrix Equation [A_1XB_1,A_2XB_2]=[C,D];
矩阵方程组[A_1XB_1,A_2XB_2]=[C,D]的最小二乘解
补充资料：非线性最小二乘拟合
分子式：
CAS号：
性质：用最小二乘法拟合非线性方程。有些变量之间的非线性模型，通过变量变换可以化为线性模型，此称为外在线性。而有些变量之间的非线性模型，通过变量变换不能化为线性模型，通称为内在非线性。对于非线性模型y=f(ξ,θ)+ε，其残差平方和。S(θ)是θ的函数，当模型关于θ是非线性的，正规方程关于θ也是非线性的。基于使残差平方和s(θ)达到极小的原理求出θ的估计值，拟合非线性回归方程。
CAS号：
性质：用最小二乘法拟合非线性方程。有些变量之间的非线性模型，通过变量变换可以化为线性模型，此称为外在线性。而有些变量之间的非线性模型，通过变量变换不能化为线性模型，通称为内在非线性。对于非线性模型y=f(ξ,θ)+ε，其残差平方和。S(θ)是θ的函数，当模型关于θ是非线性的，正规方程关于θ也是非线性的。基于使残差平方和s(θ)达到极小的原理求出θ的估计值，拟合非线性回归方程。
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参考词条