特征向量

基本解释 (translations)

eigenvector

词组短语

characteristic vector · proper vector · feature vector

与特征向量相关的网络例句 [注：此内容来源于网络，仅供参考]

First,the distance ratio between within-class and between-class of each column of mixing matrix is computed by using a distance measurement.Then,for each class,the mixing matrix and independent components are re aligned by sort ascending according to the distance rate,and the columns of the weight matrix with smaller distance ratio and the corresponding features are reserved.Finally,the best feature set is selected by genetic algorithm from these foregoing feature vectors.

首先，使用一种距离度量来计算混合矩阵每一类的类内类间距离比；然后对每一类按该比值由小到大重新排列混合矩阵和独立分量，保留权重矩阵中类间类内距离比大的列，及其对应的特征向量；最后对这些特征向量使用遗传算法选择最优特征组。

The common method, that all strong-correlation terms of the model are eliminated, can bring the loss in the engineering application, so the new method is proposed that the identified model reserves some correlation. The augmented matrix A is constructed by the outputΔW and the matrix S. The"determinating order based on ratio of determinant"is brought out to screen the strong-correlation terms in the structure identification. The latent root estimation is improved in screening the eigenvalues and eigenvectors. Thus the estimation precision is improved greatly.The consistence check of guidance instrument error coefficients of flight test and ground test is the purpose of flight experiment. The causes of inconsistency of the two models are analyzed. The hypothesis test of linear regression model based on F statistics is proposed to check the consistence.Finally, the instability of error coefficients is probably caused by the change of the flight environments, therefore, the relation between the error coefficients and flight environment is analyzed. The approach is presented to identify SINS guidance instrument error models and compensate the error in the segmented sections corresponding to the change of vertical acceleration of aircraft.

在结构辨识中，常用的方法由于将模型中的强相关项全部剔除而给工程应用带来损失，因此，本文提出了新的有益思想，即在保留一定相关性的基础上进行辨识：将输出向量ΔW与环境函数矩阵S构成增广矩阵A，然后采用"比定阶行列式"来剔除相关向量的方法，这样既可以尽可能多地保留了对落点影响大的强相关参数，又可以对落点影响小的强相关参数给予剔除；在参数估计中，改进了特征根估计中特征根和特征向量的筛选方法，提出"近零"准则，从而大大提高了参数估计的精度；再者，鉴于天地模型"一致性"检验是飞行试验和SINS制导工具误差系数分离的主要目的，因此，本文又深入分析了造成天地模型不一致的原因，提出了采用基于F统计的线性回归模型假设检验方法来进行捷联制导工具误差模型的天地"一致性"检验；最后，鉴于飞行环境剧烈变化可能会对惯性仪表误差系数稳定性带来一定的影响，因此本文深入地分析了SINS制导工具误差系数与外界环境的关系，提出了基于过载变化大小的分段辨识和分段实时补偿的算法。

The image is first reduced in dimension by Gaussian pyramid, and the Hu moment is applied to the fixed sized overlapping blocks of low-frequency image. The eigenvectors are lexicographically sorted. Then, similar eigenvectors are matched by a certain threshold value. Finally, the area threshold value is proposed to remove the wrong similar blocks. The mathematical morphology operations are performed to locate the tampered part.

该算法首先将图像进行高斯金字塔分解，将低频图像进行块分解，提取每块的Hu矩不变特征，并将特征向量排序，然后为每个特征向量搜索符合阈值的相似特征向量；最后利用区域面积阈值去除错误的相似块，并结合数学形态学定位篡改区域。

The method optimizes the features according to the class separable criterion of margin of within-class and among-class distance and selects the optimized feature vector combination based on four kinds of features,such as gray histogram feature,wavelet transform feature,gray level co-occurrence matrix feature and moment invariants feature,with 26 dimension feature vectors in all.

针对冷轧板带材常见表面缺陷图像识别的特点，提出了板带材表面缺陷多特征优化组合方法，该方法以直方图统计特征、小波变换特征、灰度共生矩阵特征、不变矩特征等4类特征共26维特征向量为基础，依据类间类内距离差的类别可分离性判据对特征进行优化，选出最优特征向量组合。

In the solution process, the basis vectors can be obtained from the matrix perturbation or the Neumann series, and then the Epsilon-algorithm was used to obtain the approximate eigenvectors.

在求解过程中，利用Neumann级数产生基向量，然后用Epsilon算法求出近似特征向量，最后用Rayleigh商分析，求出了修改后结构的近似特征值和特征向量。

It is also the base of structure optimization.According to main object characteristics of structural dynamics design,structuraleigenveetor sensitivity computation is,first,be studied.A Neumann series expansionmethod is proposed in accordance with the modal method and is developed to agreewith the structure with rigid body modes.A rapid Neumann series expansion methodfor eigenvector sensitivity is formed;it may greatly improve the efficiency ofcomputing eigenvector derivation.

本文根据结构动力学设计的主要目标特性，首先进行了结构特征向量灵敏度分析研究：形成了计算特征向量灵敏度的Neumann级数展开法，并将其扩展到适用于具有刚体模态的结构特征向量灵敏度分析；研究出计算特征向量灵敏度的快速Neumann级数展开法，该方法大大提高了特征向量灵敏度分析的效率；建立了无需K-1运算的结构特征向量灵敏度分析的共轭梯度迭代法。

A Schmidt orthogonalization transfer is performed to feature vectors of S W. This process eliminates the correlation between vectors and improves the classification ability for unclassified samples that located in the small angle between vectors.

对Sw矩阵的特征向量使用Schmidt正交变换化，这一过程消除了向量间的相关性从而提高了位于特征向量坐标轴小夹角内样本点的分类性能。

The multi-dimensional signals measured by sensors with a machine working under normal condition or fault condition, can be used to build Q statistic and T^2 statistic respectively to detect the state of the machine. Meanwhile, the feature vector is constructed by the Q value and the T^2 value, the geometrical distance between the normal feature vector and the feature vector H when the machine goes wrong is used to realize the fault location. The results show that the method is feasible.

利用多个传感器测量的异步电机多维信号参量，构建电机在正常工作和发生故障时的Q统计和T^2统计，以实现电机的状态检测；利用Q统计和T^2统计值构建电机的状态特征向量，通过比较度量当前电机的特征向量与电机发生故障时的特征向量H的几何距离来实现电机故障的定位与分离实验证明，该方法可以有效地实现故障的诊断与分离。

Wavelet packet decompresses vibration signals, and wavelet packet nodes which are sensitive to the change of state, as eigen-nodes, form a decomposition tree; the signal restructured at all eigen-nodes can be used as the fingerprint in many breaker diagnostic systems. Support vector machine with the "most important" factors at eigen-nodes as input vector classifies the states at "one to others" strategy.

首先利用小波包分解振动数据，提取状态变化敏感节点作为特征节点形成分解树，利用敏感节点重构完好状态振动信号，并以此作为当前大多断路器诊断系统中使用的指纹信号；同时提取特征节点最大系数形成特征向量，作为支持向量机的输入向量，使用&一对其余&策略进行特征分类。

The proposed MRSVQH algorithm quantizes feature vectors according to the L\-2 norms of the randomized sub-vectors and hashes feature vectors to their corresponding hash buckets. Such index structures are built for multiple times in order to increase the searching accuracy.

该算法根据随机选择的若干子向量的L\-2范数对特征向量进行量化，并根据量化值对特征向量进行散列，构建出哈希索引结构；为了提高搜索精度，类似的哈希索引结构被多次构建。

与特征向量相关的网络解释 [注：此内容来源于网络，仅供参考]

Characteristic vector：特征向量

Characteristic root, 特征根 | Characteristic vector, 特征向量 | Chebshev criterion of fit, 拟合的切比雪夫准则

Generalized characteristic vector：广义特征向量

广义特征值 Generalized characteristic value | 广义特征向量 Generalized characteristic vector | 逆矩阵 inverse matrix

characteristic value, characteristic vector：第五章、矩阵的特征值和特征向量

第四章、向量空间 vector space | 第五章、矩阵的特征值和特征向量 characteristic value, characteristic vector | 第六章、二次型 quadratic form

circulant matrix eigenvector：循环矩阵特征向量

循环矩阵特征值 circulant matrix eigenvalue | 循环矩阵特征向量 circulant matrix eigenvector | 循环缓冲 circular buffer

Eigenvector：特征向量

在实际的工程计算中,经常会遇到求n阶方阵A的特征值(Eigenvalue)与特征向量(Eigenvector)的问题. 对于一个方阵A,如果数值λ使方程组由于根据定义直接求矩阵特征值的过程比较复杂,因此在实际计算中,往往采取一些数值方法.

eigenvector basis：特征向量的基

eigenvector 特征向量 | eigenvector basis 特征向量的基 | elementary matrix 初等矩阵

eigenvector basis：特征向量基

output basis 输出基 | eigenvector basis 特征向量基 | input coordinate 输入坐标

complex eigenvector：复特征向量

complex eigenvalue 复特征值 | complex eigenvector 复特征向量 | complex exponential 复指数

eigenvector analysis：特征向量分析

effective radius of the Earth 地球有效半径 | eigenvector analysis 特征向量分析 | eigenvector 特征向量

proper vector：特征向量

proper value 本征值 | proper vector 特征向量 | proper vibration 固有振动