查询词典 covariance matrix

Missirlis in article [1]. At the same time, a sufficient condition for convergence of the PSD method is given to be compared when the coefficient matrix A of the linear system Ax = b is a symmetric, positively defective matrix. In §3.2, an example is given to state that the range of our sufficient condition is wider than theorem 3.3 of article [1]. On the other hand, following a.n analogous approach of [14] and starting the functional relationshipwe have a perfect analysis for the PSD method to converge and optimum valves for the involved parameters under different conditions.Under the assumptions that A is a consistent ordered matrix with nonvanishing diagonal elements and the eigenvalues of the Jacobi matrix of A are real,we get necessary and sufficient conditions for the PSD method to convergence.The result is equal to theorem 1 of article [9].Under the same condition, we can see the optimal parameter and of corresponding spectral radius of thePSD method in [8]:(2)When A is a consistent ordered matrix with nonvanishing diagonal elements and the eigenvalues of the Jacobi matrix of A are imaginary or zero,we get necessary and sufficient conditions for the PSD method to convergence.In chapter 3, the optimal parameter and of corresponding spectral radius of the PSD method are given by table 3.3. Moreover, under the assumption 0

Missirlis在文献[1]中定理3.3的不准确，同时给出了当线性方程组Ax=b的系数矩阵A为对称正定阵时，PSD迭代法收敛的一个充分条件与之比较，并且在§2.3中用实例说明了对于一部分矩阵而言本文得到的充分条件广于[1]中定理3.3的充分条件；另一方面，按照文献[14]的方法，我们从PSD迭代法的特征值λ与其Jacobi迭代矩阵B的特征值μ的关系式：出发，在不同条件下对PSD迭代法的收敛性和最优参数以及最优谱半径进行了完整的分析：(1)在系数矩阵A为(1，1)相容次序矩阵且对角元全不为零，其Jacobi迭代矩阵B的特征值全为实数的条件下，给出了PSD迭代法收敛的充分必要条件，此结果与[9]中的定理1等价，此时最优参数及最优谱半径由[8]得：(2)第三章表3.3中给出了，当系数矩阵A为(1，1)相容次序矩阵且对角元全不为零，其Jacobi迭代矩阵B的特征值全为纯虚数或零时的PSD迭代法的收敛范围和最优参数，并且我们可以得到当0

The method for constructing LDPCC code includes encoder and decoder. Operational steps include as following: determining parameters of checkout matrix HQT of group code of permutation unit matrix LDPC; constructing checkout matrix HQT of group code of permutation unit matrix LDPC; constructing checkout polynomial matrix HT of LDPCC code; exporting generated matrix G of LDPCC code.

一种LDPCC码的构造方法，包括编、译码器，运行的步骤包括：初始化的步骤；确定一个置换单位矩阵LDPC分组码的校验阵HQT的参数的步骤；构造置换单位矩阵LDPC码校验阵HQT的步骤；构造LDPCC码的校验多项式矩阵HT的步骤；导出LDPCC码的生成矩阵G的步骤。

The method according to claim 6, for manufacturing a blade wherein a part external to the shell comprises a blade rooting part having at least one attachment bar, in which an end portion for linkage to a hub of said rotor is fitted with a laminate attachment, also comprising thermoplastic composite, and extending said at least one spar bar housed in the shell, the method further comprising the step of producing the elementary piece comprising the at least one spar by producing each attachment bar and the spar bar in the form of a strip of continuous and unidirectional reinforcing fibres which are agglomerated by said thermoplastic matrix, obtained by pultrusion at a melting temperature of the matrix, of several plies of continuous and unidirectional fibres which are agglomerated by said matrix, and, if said rooting part comprises at least two attachment bars, each one of which respectively extends one spar bar, in superimposing parts of corresponding strips forming the spar part housed in the shell, with interposition of at least one ply of continuous and unidirectional fibres which are agglomerated by said matrix between the adjacent superimposed strip parts, and fitting a removable tool, in the shape of a wedge, between parts held spaced from each other of two neighbouring strips, to form two attachment bars of the rooting part, then arranging the strips in a mould, and inserting each linkage end portion of a pultruded strip between two laminate elements of the attachment, to superimpose all the laminate elements of the attachment, closing the mould and compacting its contents under pressure and at a melting temperature of said thermoplastic matrix, and solidifying the matrix by cooling to rigidify the spar thus obtained with the laminate attachment solidly attached to its rooting part.

该方法根据索赔六，制造业刀片其中的一部分，罐壳外部包括一个叶片生根的一部分，拥有至少一个实习酒吧，在其中一结束部分为联系枢纽的说，转子是带有附件的层压板，也包括热塑性复合材料，并延长说，至少有一石酒吧住在蚬壳公司，进一步组成的方法步骤，生产初级一块组成的至少有一个SPAR公司生产每个附件的酒吧和酒吧在SPAR公司的形式，带不断加强和单向纤维是由烧结说，热塑性矩阵，得到的拉挤在熔化温度矩阵，数层的连续和单向纤维是由烧结说，矩阵，如果说，生根部分组成，至少有两个实习酒吧，每个人都分别延长一石酒吧，在叠加部分，相应的带形成的SPAR公司的一部分，居住在蚬壳公司，与干预的至少有一单层连续和单向纤维是由烧结说，矩阵之间的毗邻叠加条形零件，及装修一个可移动的工具，在形状，离间，部分之间举行的间隔从对方的两个邻国带，形成两个附件酒吧的生根部分，然后安排带在一个模具，并插入每个联系年底部分一拉挤带之间的二板的要素扣押，叠加所有层压板的要素扣押，关闭模具和压缩其内容的压力下和在熔化温度说，热塑性矩阵，并巩固矩阵冷却 rigidify该SPAR公司，从而获得与层压板的附着牢固重视其生根的一部分。

The proposed method first employs covariance matrix tapers to obtain the interference covariance function and its corresponding spatial spectrum in non-ideal scenarios.

该方法首先利用协方差矩阵加权得到非理想情况下的干扰协方差函数和对应的空间谱，然后根据周期延拓后的干扰空间谱估计干扰自由度。

Collocation needs to know the covariance matrix of the signal vector,which is usually evaluated by a chosen covariance function and corresponding coefficient fitting.

拟合推估解算必须首先求得信号向量的方差协方差矩阵，该协方差矩阵一般通过选定的协方差函数，并通过已测点数据进行拟合得到。

Since the size of image covariance matrices is same as that of images and the within-class image covariance matrix is usual nonsingular, thus, the difficulty resulting from high dimensionality and singular case are artfully avoided.

该文提出了一种基于复向量进行特征表不的并行融合新策略，建立了复线性投影分析的理论，给出了复特征空间内进行线性特征抽取方法，事实上，经典的线性投影分析理论与方法只是本文理论与方法的一个特例。

For these important and practical problems,sample covariance matrix is an important statistic because many important statistics are functionals of it.When we make statistical inferences,such as estimations and/or hypothesis tests,the sample covariance matrices must be investigated.

以上诸多方面在做估计和假设检验过程中，无不用到大维随机矩阵的处理，尤其是大维样本协方差矩阵的相关性质，因为多元分析中许多重要的统计量都可以表示成样本协方差矩阵的函数。

Meanwhile, in order to fix defect of former methods, an optimal source array weight matrix is presented to estimate Green's function matrix by analyzing the estimation covariance of Green's function matrix.

同时指出了现有方法存在的不足，并在此基础上通过研究格林函数矩阵的估计协方差，提出了一种最佳估计算法。

In this dissertation, we develop the research work of the following three aspects based on existed work.First, using the scale-invariant property of multiscale model, i.e. Markovian among scales, a method of qth-order tree-based for multiscale representation of a class of 1-D stochastic process is presented. The multiscale stochastic model is established. The representation forms of parameter matrices, such as, the state transition matrix, the disturbance matrix, the initial state and the corresponding covariance matrix are deduced in detail. The multiscale sample paths based on distinctive order tree are presented by computer simulation.

本文在已有工作的基础上，开展了以下几个方面的研究工作： 1、根据多尺度模型尺度不变性，即利用尺度间的Markov性，给出了一类1-D随机过程基于一般q阶树的多尺度表示方法，建立了相应的多尺度动态模型，详细推导了多尺度模型中的状态转移阵、扰动阵、初始状态和相应的协方差阵，并通过计算机仿真给出了不同阶树的多尺度采样路径。

A new approach-two dimensional principal component analysis(2DPCA) is developed for face recognition. 2DPCA approach dont need to transform the image matrix into feature vector. It directly uses the image matrix to construct the sample covariance matrix.

5研究了二维主成分分析(2DPCA)方法，2DPCA方法不需要先将图像矩阵展开成一维向量，而是直接利用图像矩阵来构建样本协方差矩阵，所以2DPCA比PCA的识别时间更短，识别更高。

Based on the construction of international financial centre, we have pushed forward the explorations in the integration of cultural industries with financial services.

2010年及整个"十二五"期间，上海文化产业面临更有利的发展时机、更坚实的发展基础和更完善的发展环境。

The koala is an animal with a large bag in its body.

树袋熊用它的身体的一个大的袋子是一只动物。