搜索资源列表
COMPDIR
- % COMPDIR Computes a search direction in a subspace defined by Z. % Helper function for NLCONST. % Returns Newton direction if possible. % Returns random direction if gradient is small. % Otherwise, returns steepest descent direction. % I
Eigenvalue
- 用QR分解来求矩阵的全部特征值 包括:QR分解,矩阵转置,;矩阵求逆,矩阵相乘,最后迭代得出特征值-With QR decomposition to the full matrix eigenvalue include: QR decomposition, matrix transpose, matrix inversion, matrix multiplication, and finally reached eigenvalue iteration
QR
- 在对矩阵进行拟上三角化的基础上利用带双步位移的QR分解法求解矩阵的特征值-To be in the upper triangular matrix based on the use of dual-band step-by-step displacement of QR decomposition method for solving matrix eigenvalue
saperate4
- 基于广义特征值和核函数的非线性盲分离算法.pdf-Based on the generalized eigenvalue and nuclear function algorithm for nonlinear blind source separation. Pdf
JocobiGG
- 本程序是根据jacobi过关法求实对称矩阵的全部特征值和特征向量-This procedure is based on realistic method jacobi clearance of all symmetric matrix eigenvalue and eigenvectors
LinearAlgebra
- 线性代数基本实现,各种基础的线性代数计算接口(加减乘除、求逆、求秩、最大线性无关组)以及矩阵的特征值特征向量的计算(QR方法)。含有设计文档。-The basic realization of linear algebra, and linear algebra calculation based interface (addition and subtraction multiplication and division, inverse, and rank, the largest grou
read
- 基于变异系数权重的TOPSIS法,根据评价指标特征值矩阵 构成规范化的矩阵 及规范化的加权矩阵Z。-Coefficient of variation based on the weight of the TOPSIS method, based on the evaluation indicator matrix eigenvalue of the matrix constitutes a standardization and the standardization of the weight
tezhengzhi
- 矩阵特真问题求解,用于解矩阵的特征值,根据数值计算方法得到-Matrix special real problem solving for the solutions of eigenvalue, numerical methods are based
PCA
- PCA主成分分析算法用于人脸识别的例子。此算法是基于矩阵特征值的排序而提取出主要成分的。-PCA principal component analysis algorithm for face recognition example. This algorithm is based on the ranking of the matrix eigenvalue extracted principal components.
chepaizifufenge
- 提出了一种改进的基于垂直投影特征值的 分割算法-An improved vertical projection based segmentation algorithm for eigenvalue
eigenanalysispse_test
- 基于矩阵特征值分解的谱分析方法,很有参考价值-Matrix eigenvalue decomposition based spectral analysis method, a good reference
Advanced-optical-simulation
- 高等光学仿真中的程序代码,介绍了光纤的本征模以及激光原理,并以Matlab为基础仿真。-Advanced optical simulation program code, introduced the optical fiber the eigenvalue of the mode and laser principle, and based on Matlab simulation.
music
- MUSIC算法是一种基于矩阵特征空间分解的方法。从几何角度讲,信号处理的观测空间可以分解为信号子空间和噪声子空间,显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成,噪声子空间则由协方差矩阵中所有最小特征值(噪声方差)对应的特征向量组成-MUSIC algorithm is a feature space based on matrix decomposition method. From the geometric point of view, the o
t1
- 基于最小 最大特征值的改进的高分辨MUSIC算法 不需要信源的估计数目-Based on the estimated number of minimal and maximal eigenvalue improved high-resolution MUSIC algorithm does not require source
classical_music_1
- MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲,信号处理的观测空间可以分解为信号子空间和噪声子空间,显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成,噪声子空间则由协方差矩阵中所有最小特征值(噪声方差)对应的特征向量组成。-MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of vie
classical_music_2
- MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲,信号处理的观测空间可以分解为信号子空间和噪声子空间,显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成,噪声子空间则由协方差矩阵中所有最小特征值(噪声方差)对应的特征向量组成。-MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of vie
ELSA_v0.1.tgz
- This package contains the code for the Enhanced Local Subspace Affinity (ELSA) with Enhanced Model Selection (EMS+) and number of motions estimation based on the eigenvalue spectrum of the Symmetric Normalized Laplacian matrix.-This package co
MUSIC
- MUSIC算法是一种基于矩阵特征空间分解的方法。从几何角度讲,信号处理的观测空间可以分解为信号子空间和噪声子空间,显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成,噪声子空间则由协方差矩阵中所有最小特征值(噪声方差)对应的特征向量组成。MUSIC算法就是利用这两个互补空间之间的正交特性来估计空间信号的方位。噪声子空间的所有向量被用来构造谱,所有空间方位谱中的峰值位置对应信号的来波方位。MUSIC算法大大提高了测向分辨率,同时适应于任意形状的天线阵列,但是原
Matrix-eigenvalue-problems
- 矩阵特征值问题的数值解法在matlab程序中的实现代码-Matrix eigenvalue problems of numerical solution based on Matlab
uniform-circular-array
- 对一维角度估计采用MUSIC算法 ,通过谱峰搜索得到信号的方位角 对二维角度估计采用模式激励法 ,对均匀圆形阵列的输出信号进行模式激励 ,使其阵列流形具有类似于均匀线性阵列的形式 ,在此基础上 ,对波达矩阵进行特征分解 ,由各特征值对应的特征向量处理得到对应信号的到达方向 .给出的计算机仿真结果证实了它们的有效性 . -This paper studies the DOA estimation based on uniform circular bursts out of a one-dimen