例如，它没有返回矩阵的特征值的函数。
For example, no function returns the eigenvalues of a matrix.
这些被称为特征值。
研究一类特征值问题及其应用。
迭代方法可用来确定特征值。
An iteration procedure can be used to determine the eigenvalue.
最后，浅谈了矩阵逆特征值问题的应用。
Finally, we simply talk about the application and development of inverse eigenvalue problem.
由矩阵的特征值，求取椭圆率和线性因子。
We can get the ellipticity and linearity from the eigenvalues of the covariance matrix.
在数学中，当你看到许多值的解法时，这些就是特征值。
In math, when you see multiple value solutions, these are eigenvalues.
对非线性特征值的求介本文提出了一种方法。
In this paper a method is presented for solving Non-linear Eigenvalue problems.
其中包括计算矩阵的特征值问题，误差问题等。
Including the calculation of matrix eigenvalue problem, error and so on.
本文主要讨论了两类结构矩阵的逆特征值问题。
In this paper, two kinds of structure inverse eigenvalue problems are discussed.
特征值问题的计算方法是科学计算基本论题之一。
The calculation of eigenvalue problem is one of the basic topics of scientific computing.
利用协方差矩阵的特征值。能实现信号和噪声的分解。
The eigenvalue of covariance matrix can be used to separate signal from noise.
假如操作者光谱是发散的，可见仅能获得那些离散特征值。
If the operator's spectrum is discrete, the observable can only attain those discrete eigenvalues.
线性转换及线性运算子，特征值扩展，以矩阵表示线性运算子。
Linear transformations and linear operators, eigenvector expansion, matrices as representations of linear operators.
矩阵迭代法是求矩阵的第一阶特征值与特征向量的一种数值方法。
Matrix iteration method can be employed to figure out the first eigenvalue and eigenvector of a matrix.
如果像素是局部的最大值或者最小值，则会被作为备选的特征值。
If the pixel is a local maximum or minimum, it is selected as a candidate keypoint.
本文提出的复杂特征值检测算法降低了单一特征值检测的误报率。
Complicated signature detection improves false positive rate compared with single signature detection.
本文利用数学定理和新的灵敏度定义给出特征值和特征向量导数的精确解。
The exact solution of derivatives of eigenvalue and eigenvector is presented by utilizing the mathematical theorem and new definitions of sensitivities.
通过逐一考查全体特征值，实现图同构的判定并确定同构图的顶点对应关系。
After all the eigenvalues have been considered, isomorphism will be determined and correspondence of vertices in isomorphic graphs can be ultimately identified.
分析了一般粘弹结构特征值问题的特点，建立了一般粘弹结构的模态分析方法。
The characteristics of the eigen value problem of general viscoelastic structure are analyzed and its modal analysis method is put forward in this paper.
采用龙格—库塔法确定系统的响应过程和采用Q-R法求解系统的全部特征值。
The responses of all elements of FAS system are determined by Runge-Kutta method and all eigenvalues are solved by the Q-R method.
为了定位特征值的指向，会使用到一个计算出的周围特征值的变换指向直方图来表示。
To determine the keypoint orientation, a gradient orientation histogram is computed in the neighbourhood of the keypoint.
多层前馈人工神经网络在装备故障诊断中的应用含设备运行状态特征值设定和故障判定。
The application of multi-layer feed-forward artificial neural network in fault equipment diagnosis includes feature value setting of equipment operation condition and fault judgment.
对角矩阵及三角矩阵之特征值，相似矩阵，由QR分解计算特征值，主特征值之迭代估算。
Eigenvalues of diagonal and triangular matrices, similarity transforms, calculation of eigenvalues from QR decomposition, iteratively estimating the leading eigenvalue.
本文指出，在矩阵迭代法的迭代过程中，特征值近似值序列是单调收敛的，并给出计算实例。
This paper indicated that the sequence of approximate value of eigenvalue is monotone convergent in the iteration process of matrix iteration method. And also gave examples.
导出了计算系统最小衰减率的公式，该公式将系统矩阵的特征值与最小衰减率直接联系起来；
The minimum decay coefficient of PM concentrations is computed by a formula, which is found in direct link to the minimum decay rate with the eigenvalues of the state matrix.
然后，采用一种基于模匹配过程的横向散射矩阵方法，系统分析了微带导波结构的特征值问题。
Then, a transverse scattering matrix approach based on mode matching technique is used to analyze the eigenvalue problem of microstrip guiding wave structures.
而要检测软故障，就要针对电路中的软故障信号进行分析，提取能够反映软故障信号的特征值。
But it can not make good analysis of the soft fault signal in the circuits which is random and unstable or can not reflect and extract the transient characters of the soft fault signal.
而要检测软故障，就要针对电路中的软故障信号进行分析，提取能够反映软故障信号的特征值。
But it can not make good analysis of the soft fault signal in the circuits which is random and unstable or can not reflect and extract the transient characters of the soft fault signal.
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