- 更多网络例句与本征空间相关的网络例句 [注：此内容来源于网络，仅供参考]
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The new method of the singular value decomposition is used to calculate the eigenvalue of embedding space matrix , and the corresponding algorithm to calculate eigenvectors and to obtain the basis of embedding vector space is put up in this paper.
应用本征值分解技术对动力系统实测数据嵌入空间矩阵的本征值进行了计算，提出了具体计算嵌入空间矩阵本征值及其本征向量的改进计算方法，以及嵌入空间矩阵基的改进选取方法。
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Using the method of dividing variables , the solution of elastic dynamics can be changed into the eigen value problem of Hamilton s space differential operator matrix , and the total solution of dual variable s (modal strain and modal strain rate ) can be obtained by .
采用分离变量方法，将弹性动力学解转变为Hamilton空间算子矩阵的本征值问题，对偶变量的全解通过本征解来展开而获得。
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Based on the array signal processing and the eigenstructure decomposition method, the signal subspace and the noise subspace are achieved by reconstructing and decomposing the data matrix collected by the array sensors. By using the orthogonal properties of the two subspaces, the information about the velocities and bearings of spatial moving targets can be exactly obtained.
本文提出一种超分辨的二维谱估计方法，它利用的是阵列处理技术，对天线接收数据进行本征分析，将信号数据张成一个空间并对其进行分解，根据正交原理分割成信号子空间和噪声子空间，构造出噪声本征矢量，利用信号子空间与噪声子空间的正交性，在空间谱上形成极值点，从而达到对方向和速度的二维高分辨率估计。
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In this case the Hilbert space can be decomposed into an orthogonal direct sum of partial wave subspaces, which are the simultaneous eigenspaces of the angular momentum operators J^2,J_3 and the operator K which describes the spin-orbit coupling.
在此情形下希尔伯特空间可被分解为部分波子空间的直角方向的和，这子空间是本征角动量算符J^2，J_3和描述自旋轨道耦合算符K同时存在的本征空间。
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The critical buckling loads and buckling modes are described by eigenvalues and eigensolutions in symplectic space.
在辛几何空间中，将圆柱壳的临界屈曲载荷和屈曲模态归结为辛本征值和本征解问题。
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When the Hamiltonian system is introduced into the research of dynamic buckling in cylindrical shells, the problem uses dual variables in Symplectic space instead of variables in traditional Euclidean space. The symplectic eigenfunction expansion method gives out an exact solution space. So many problems are solved, which can not find the solution in traditional methods.
将哈密顿体系引入结构的动态屈曲研究中，其意义在于将结构的动态屈曲研究从传统的欧几里得几何空间进入到由原变量和对偶变量组成的辛几何空间之中，从而使辛本征函数展开的直接解析法得以实施，这样就可以求解许多以往半逆凑合法无法求解的问题，并且所得到的解空间是完备的。
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Firstly, kinetic energy and potential energy expressions are obtained by a series of derivation, considering the extrinsic momentum. In the symplectic system, control equations of longitude and transverse coupling vibrations of rotating elastic structures, or Hamilton canonical equations are described. So the solution of the problem is boiled down to the eigenvalues and eigensolutions in the symplectic space.
首先，从能量的观点出发，通过一系列的理论推导，给出旋转结构的动能和势能表达式，并考虑外动量的影响，从而得到在辛体系中描述旋转结构纵向和横向耦合振动的控制方程，即哈密顿正则方程，于是将问题归结为辛几何空间的本征值和本征向量问题。
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A Hamiltonian system was constructed for the fundamental problem of the dynamic buckling ,thereby problems of critical loads and buckling modes were reduced to determinations of eigenvalues and eigensolutions in a symplectic space and a symplectic method was proposed.
针对有内压或外压的弹性圆柱壳在轴向冲击载荷耦合作用下的动态屈曲问题，构造哈密顿体系，在辛空间中将临界载荷和动态屈曲模态归结为辛本征值和本征解问题，从而形成一种辛方法。
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In this framework, image frame, audio and text are represented, which are the three modalities in video shots as data points by the 3rd-order tensor. Then a subspace embedding and dimension reduction method is proposed, which explicitly considers the manifold structure of the tensor space from temporal-sequenced associated co-occurring multimodal media data in video. It is called TensorShot approach. Transductive learning uses a large amount of unlabeled data together with the labeled data to build better classifiers. A transductive support tensor machines algorithm is proposed to train effective classifier. This algorithm preserves the intrinsic structure of the submanifold where tensorshots are sampled, and is also able to map out-of-sample data points directly. Moreover, the utilization of unlabeled data improves classification ability.
在此框架中，视频镜头首先被表示成由视频中所包含的文本、视觉和听觉等多模态数据构成的三阶张量；其次，基于此三阶张量表达及视频的时序关联共生特性设计了一种子空间嵌入降维方法，称为张量镜头；由于直推式学习从已知样本出发能对特定的未知样本进行学习和识别，最后在这个框架中提出了一种基于张量镜头的直推式支持张量机算法，它不仅保持了张量镜头所在的流形空间的本征结构，而且能够将训练集合外数据直接映射到流形子空间，同时充分利用未标记样本改善分类器的学习性能。
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Instead of statistical description, a feature subspace with the base of some significant eigen vector extracted from covariance matrix is constructed to describe speech feature distribution of speaker. Distance metrics for measuring distance between input feature vector and subspace are also proposed for pattern matching.
说话人语音训练样本提取特征后在语音特征观察空间形成具有一定散度的分布，根据主元分析原理和分布散度提取主要散度本征向量作为基底构成说话人语音特征子空间，并通过测试语音特征矢量与子空间的距离测度进行模式匹配。
- 更多网络解释与本征空间相关的网络解释 [注：此内容来源于网络，仅供参考]
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generalized Bayes decision function：广义贝叶斯决策函数
广义贝叶斯估计|generalized Bayes estimate | 广义贝叶斯决策函数|generalized Bayes decision function | 广义本征空间|generalized eigenspace
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eigenspace：本征空间
通过MultiNet,可以使用几种本征空间(eigenspace)的方法来分析网络的结构. MultiNet包含四种统计技术:交叉表和卡方检验,ANOVA,相关和p*指数随机图模型. StOCNET是个WINDOWS环境下的开放软件系统,适用于社会网络的高级统计分析.
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generalized eigenspace：广义本征空间
广义贝叶斯决策函数|generalized Bayes decision function | 广义本征空间|generalized eigenspace | 广义本征值|generalized eigenvalue
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Eigenraum eigenspace：本征空间,特征空间
Eigenfrequenz natural frequency; eigenfrequency 自然频率 | Eigenraum eigenspace 本征空间,特征空间; | Eigenschaft property 特性