广义最小二乘,generalized least squares
1)generalized least squares广义最小二乘
1.This article discusses the technology of least squares andgeneralized least squares,and compare them at last.分析最小二乘(LS)和广义最小二乘(GLS)的原理,并对两种算法进行了比较。
2.Based on nonlinear error equations of adjustment functions model,and making use of the theory ofgeneralized least squares,this paper gives a new adjustment model by parameters model with initial data error.基于平差模型的非线性函数误差方程,利用广义最小二乘原理,给出了一种新的顾及起算数据误差的参数平差模型,与将起算数据视为无误差的一般非线性测量参数平差解算,或虽考虑起算数据误差,但基于线性误差方程形式,将其与观测值一起进行整体平差的方法相比,在理论和方法上都有较大创新。
英文短句/例句
1.generalized least squares estimator广义最小二乘估计量
2.joint GLS coefficient estimator联合广义最小二乘系数估计量
3.THE GENERALIZED LEAST SQUARES DECONVOLUTION METHOD IN COMPTON PROFILE MEASUREMENT康普顿轮廓测量中的广义最小二乘法退卷积
4.GLS Parameter Identification and Simulation Based on Matlab基于MATLAB的广义最小二乘参数辨识与仿真
5.Generalized least squares support-vector-machine algorithm and its application一种广义最小二乘支持向量机算法及其应用
6.Dynamic Modeling of Acceleration Sensor Based on Generalized Least Square Method基于广义最小二乘法加速度传感器的动态建模
7.To Solve the Project s Investment Regression Analysis with General Least Squares;利用广义最小二乘法解决项目投资回归分析中的自相关问题
8.Recursive fixed memory generalized least squares method and simulation research广义最小二乘限定记忆参数辨识方法与仿真研究
9.The Relative Efficiencies of a Class of Biased Estimetor with Repect to Generalized Least Square Estimator in Linear Model回归系统的一种有偏估计与广义最小二乘估计的相对效率
10.Generalized Least Squares Estimates of Parameters In Heteroscedastic Regression Model under Linear Constraint线性约束下的异方差回归模型参数的广义最小二乘估计
11.On Robustness of GLSE in Terms of Error Distributions in Linear Model线性模型中广义最小二乘估计关于误差分布的稳健性
12.To Get M-P General Inverse Matrices by Projection of Least Square Solution;求M—P广义逆的最小二乘解投影法
13.The Questions of Least-squares and the Optimal Approximation of the Generalized Reflexive Matrices and Generalized Anti-reflexive Matrices;广义自反矩阵和广义反自反矩阵的最小二乘及其最佳逼近问题
14.The Meshless Method with Modifying Generalized Moving Least Squares Approximation;改进广义移动最小二乘近似的无网格方法
15.The Least-squares Solution of the Inverse Problem for General-antisymmetrizable Matrix;广义可反对称化矩阵反问题的最小二乘解
16.Least-squares Solution for Inverse Problem of Generalized Symmetric and Self-orthogonal Similar Matrix广义对称自正交相似矩阵反问题的最小二乘解
17.Perturbation analysis for generalized indefinite least squares problems广义不定最小二乘问题的扰动分析(英文)
18.The Least-squares Problem of General-symmetrizable Matrices and General-antisymmetrizable Matrices;广义可对称化矩阵和广义可反对称化矩阵最小二乘问题
相关短句/例句
generalized least square method广义最小二乘法
1.In this paper,the estimation of coefficient functions in a varying-coefficients EV model are constructed by using kernel smoothing andgeneralized least square method.利用核函数法和广义最小二乘法给出了一般变系数EV模型系数参数的估计,得到了估计的强相合性。
2.For the important role of mathematical model in data processing of dynamic calibration and combining the experimental data of dynamically calibrating acceleration sensor test system based on the Hopkinson bar,ageneralized least square method of dynamic model is introduced.针对数学模型在动态校准实验数据处理中的重要地位,结合Hopkinson杆对加速度传感器测试系统动态校准所得实验数据,介绍了一种基于广义最小二乘法的动态数学模型建立方法。
3)least square generalized inverse最小二乘广义逆
1.Based on the relation between synergetic learning algorithms and generalized inverses,all the algorithms for computingleast square generalized inverses can be considered as synergetic learning algorithms,which enriches the variety of synergetic learning algorithms considerably.在讨论协同学习算法和广义逆关系的基础上，指出了最小二乘广义逆的求解算法都可以看作是协同学习算法，从而大大丰富了协同学习算法的种类。
4)generalized least square method广义最小二乘方
5)generalized least squares method广义最小二乘法
1.According to the characteristics of data collection,generalized least squares method was used to calibrate model.根据采集数据的特点,采用广义最小二乘法标定模型。
6)generalized extend least squares广义增广最小二乘法
延伸阅读
广义最小二乘估计用迭代的松弛算法对线性最小二乘估计的一种改进。线性最小二乘估计在模型误差为相关噪声时是有偏估计，即其估计值存在偏差。这时采用广义最小二乘估计能获得较精确的结果。假设所讨论的单输入单输出系统的差分方程模型是式中{uk}和{yk}分别是输入和输出序列：和是算子多项式,它们的系数是需要通过估计来求出的未知数;z-1是单位延迟算子；{ek}是误差序列，它是零均值平稳相关噪声序列。为了进行广义最小二乘估计可以从形式上把ek变换成，这里，它的系数也是未知的。如果{ek}具有有理谱密度,则可把{εk}当作白噪声序列来处理。这样就把系统模型变成相应的估计准则是 广义最小二乘估计就是使估计准则J为极小的参数估计。多项式A(z-1)、B(z-1)和C(z-1)的系数都是未知的,所以不能用一个线性算法获得广义最小二乘估计。广义最小二乘估计采用迭代的松弛算法：先行固定C(z-1)，估计A(z-1)和B(z-1),使J 趋于极小；然后固定A(z-1)和B(z-1),估计C(z-1),使 J 趋于极小。如此反复迭代，直至估计值收敛。这时每步只进行简单的线性最小二乘估计运算，迭代的初值取扗(z-1)=1。广义最小二乘估计算法的估计精度高，已得到应用并获得不少成果。它的缺点在于：当信噪比较小时,J可能有多个局部极小点，估计结果不能保证收敛到全局最小点，即参数真值；它的计算量也比线性最小二乘估计增加很多。这种算法也可推广到多输入多输出系统，并且有相应的近似递推估计算法。当误差{ek}为正态噪声序列时，这种算法还可以解释为极大似然估计的松弛算法。参考书目G.G.哥德温、R.L.潘恩著，张永光、袁震东译：《动态系统辨识：试验设计与数据分析》，科学出版社，北京，1983。(G.C.Goodwin and R.L.Payne，Dynamic SystemIdentification：Experiment Design and Data Analysis, Academic Press, New York,1977.)
