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squares相关的网络例句

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与 squares 相关的网络例句 [注:此内容来源于网络,仅供参考]

When the random error is not subject to normal distribution, the least absolute deviation estimation is superior to the least squares estimation. In addition, the robustness of the least absolute deviation estimation is also better than that of the least squares estimation.

在随机误差不服从正态分布的问题中,最小一乘估计的统计性能优于最小二乘枯计;另外,最小一乘估计的稳健性更强。

In the fifth chapter, we propose the conditional generalized ridge-type estimator of regression coefficient in restricted linear regression model, and prove that it is superior to the restricted best linear unbiased estimator in terms of mean squares error and mean squares error matrix, we also give the choice of parameters matrix K.

第五章给出了约束线性回归模型中回归系数的条件广义岭估计,讨论了它的优良性,证明了它在均方误差及均方误差矩阵下都优于约束最小二乘估计,并给出了参数矩阵K的选择。

How many different patterns can be formed by blackening 3 squares of a grid consisting of 5x5 squares?

题目的意思就是5*5的格子里面放3个块,形成的图案一共有过少种。

Based on the projection theorem in the finite-dimensional inner product space, by making use of the generalized singular value decomposition and the canonical correlation decomposition of matrix pair simultaneously, we transform the least-squares problems of the abovementioned inconsistent matrix equations into the problems of solving the consistent matrix equations over given matrix set, and obtain the general expressions of the corresponding least-squares solutions.

S使得‖-X~*‖=‖-X~*‖,其中‖·‖为Frobenius范数,S分别表示矩阵方程 AXB=D,AXA~T+BYB~T=D,AXB+CYD=E和矩阵方程组=,=在一般矩阵集合或对称矩阵集合上不相容时的最小二乘解集合。

In Chapter 5, the least-squares solutions bisymmetric matrix set of the matrix equation ATXA = B on two linear manifolds Si ={X BSRnxn\\\XY - Z\\ = min} and S2 ={X BSRnXn\XY = Z, YtTZi = ZjYu ZtffYi = Zi,i = 1,2, Y, Z e Rnxm}, by applying the singular value decomposition of matrix and the canonical correlation decomposition of matrix pairs, we obtain a general expression of the least-squares solutions of the matrix equation ATXA = B on two linear manifolds.

在第五章,我们研究了两类线性流形S_1={X∈BSR~|‖XY-Z‖=min}和S_2={X∈BSR~|XY=Z,Y_i~TZ_i=Z_i~TY_i,Z_iY_i+Y_i=Z_i,i=1,2,Y,Z∈R~}上矩阵方程A~TXA=B的双对称最小二乘解。

The details as follow: 1.Based on rules about form error in GB, mathematical models of form error are established including straightness error on the base of minimizing zone method、least-squares method and two dots linked method ; flatness error based on minimizing zone method、lease-squares method、diagonal line and three dots method; circularity and cylindricity error based on minimizing zone method、lease-squares method、minimum circumscribed circle and maximum inscribed circle.

具体内容如下:根据国标中形状误差的相关规定建立了各种要素评定方法的数学模型包括:直线度的最小区域法、最小二乘法、两端点连线法;平面度的最小二乘法、最小区域法、对角线法、三远点法;圆度和圆柱度的最小区域法、最小二乘法、最小外接圆法、最大内切圆法。

Firstly, this paper investigated roundly the basic theory of the ambient vibration method, analyzed systematically the main methods of ambient vibration identification, and discussed their advantages and disadvantages. Then we established the finite element model of structural dynamics and test model and analyzed their mutual relation, improved MUSIC method and correlation function method in the harmonic identification, and gave the subspace method and correlation function method based on continuous model, which is multi-harmonic retrieve technique. And then, this paper also puts forward least-squares method to estimate directly structural physical and modal parameters; this approach replaced the derivative operator in the continuous time model with an equivalent approximation, and estimated the physical parameters of structures by the least-squares method. And then, we discussed the approximation methods of derivative and their convergence. Finally, this paper researched the modal parameters of a frame structure by the simulation and test. The results demonstrated that subspace method, correlation function method and the least-squares method are effective and stable respectively.

全面地研究环境振动识别方法的基本理论,系统地分析环境振动识别方法的主要方法,讨论它们的优点和不足;建立结(来源:6666AB44C论文网www.abclunwen.com)构动力学的有限元模型和试验模型,分析有限元模型与试验模型之间的相互关系;改进谐波识别中的MUSIC识别方法和相关函数识别方法,给出大型结构模态识别的连续型的子空间分解识别方法和相关函数识别方法,这个方法是多元谐波识别方法;提出直接识别结构的物理参数和模态参数的最小二乘方法,这个方法利用导数的近似值代替导数值,并运用最小二乘方法识别结构的物理参数,讨论导数近似值的选择方法,分析算法的收敛性;通过仿真计算和模型实验,研究高层框架结构的模态参数识别,验证子空间分解识别方法、相关函数识别方法和最小二乘识别方法的有效性,算法的稳定性;从理论和试验两个方面分析和研究噪声对识别精度的影响,影响识别精度的因素,以及部分模态成分的模态参数不能够被识别的原因。

The basic principle of least-squares method is to choose the model parameters minimizing model error sum of squares, thus the name "least squares method."

最小二乘法的基本原理是选择模型参数极小化模型误差平方和,因此而得名"最小二乘法"。

Least Squares Estimates of the SVD decomposition method, the valuation process can be transformed into squares overdetermined equations deal with the problem, and can be calculated singular value decomposition least squares problem.

详细说明:最小二乘估值的SVD分解计算方法,本程序可将最小二乘估值问题转化为超定方程组的问题处理,且可用奇异值分解的方法计算最小二乘问题。

Targeting the biomass rice stalk,we use linestone as catalyst and cracking catalyse the tar which was pyrolysised from the target.We build the rice straw biomass gasification tar removal catalytic cracking process model by the least squares support vector machine model and optimize the model parameters through genetic algorithm.We also has gotten the best catalytic cracking temperature and gas residence time, making the highest rate of catalytic cracking tar.We made wood biomass as a target analysis so that we can thermally crack tar,and we can use genetic algorithms to optimize the model parameters that is based on the wood biomass gasification pyrolysis tar removal process model through the least-squares fitting method,then we got the best pyrolysis temperature and equivalence ratio,so that it is possible to make the tar content reach the minianum.Based on the least squares support vector machine model,we had established the model of the biomass sawdust Compression molding process and fit the Optimization Parameters objective function of the molding process .

以生物质稻秆为对象,采用石灰石作为催化剂对稻秆热解焦油进行催化裂解,由最小二乘支持向量机模型建立了生物质稻秆气化焦油催化裂解脱除过程的模型,并用遗传算法对模型参数进行了优化,得到了最佳的催化裂解温度和气相停留时间,使得焦油催化裂解率达到最高;以生物质木屑作为分析对象,对其进行热裂解脱焦,并依据最小二乘曲线拟合方法建立了生物质木屑气化焦油热裂解脱除过程的模型,用遗传算法对模型参数进行了优化,得到了最佳的热裂解温度和当量比,使得焦油含量达到最小。

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