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lapackpp-2.5.2
- LAPACK++ 是一种代数数据库,在很多c与matlab的转换中都需要应用此数据库,应用比较广泛.-LAPACK++ is a library for high performance linear algebra computations. This version includes support for solving linear systems using LU, Cholesky, QR matrix factorizations, and symmetric eigenvalue
huazi
- 两个代码一个是demo, demo是它的小样例子, 另外一个是它的源代码. - - This is the matlab implementation of following noise level estimation: Xinhao Liu, Masayuki Tanaka and Masatoshi Okutomi Noise Level Estimation Using Weak Textur
ROOTMUSIC
- 针对非圆信号DOA估计问题,提出了一种基于实值特征值分解 (Eigenvalue decomposition,EVD)的求根MUSIC算法.首先利用非圆信号为实值信号的特点,将阵列上的接收数据及其共轭用欧拉公式转换为实值正弦与 余弦数据,然后将正弦与余弦数据进行串联,从而扩展了数据维数.由于采用实值矩阵的EVD,因此在EVD阶段的运算量简化为复值EVD的1/4.根据 EVD后获得的信号与噪声子空间的特点,对噪声子空间和导向矩阵进行重构以便于可以使用求根MUSIC算法获取对DOA的估计.仿真实验验
all_source_files
- 带双步的QR分解方法求所有特征值，以及反幂法求解对应特征向量-two-steps-qr-descomposure to solve all eigenvalues of a matrix. and antipower method to solve a engenvector correspongding to the eigenvalue
cgeev0
- 调用mkl的矩阵求本征值和本征函数,并得到计算时间-Call MKL for caculate the matrix s eigenvalue and eigenfunction，and get the efficiency
hmknbjka
- 最终的权值矩阵就是滤波器的系数，在matlab环境中自动识别连通区域的大小,利用matlab GUI实现的串口编程例子，线性调频脉冲压缩的Matlab程序,PLS部分最小二乘工具箱,包含特征值与特征向量的提取、训练样本以及最后的识别。-The final weight matrix is ??the filter coefficient, Automatic identification in the matlab environment the size of the connected ar
amunmnuu
- 搭建OFDM通信系统的框架，用于信号特征提取、信号消噪，最终的权值矩阵就是滤波器的系数，时间序列数据分析中的梅林变换工具，包含特征值与特征向量的提取、训练样本以及最后的识别，迭代自组织数据分析。- Build a framework OFDM communication system, For feature extraction, signal de-noising, The final weight matrix is ??the filter coefficient, Time serie
tewkiics
- 最终的权值矩阵就是滤波器的系数，计算加权加速度,PLS部分最小二乘工具箱,给出接收信号眼图及系统仿真误码率，包含特征值与特征向量的提取、训练样本以及最后的识别，有小波分析的盲信号处理，随机调制信号下的模拟ppm。- The final weight matrix is ??the filter coefficient, Weighted acceleration, PLS PLS toolbox, The received signal is given eye and BER simulati
qpapyfhz
- 用于特征降维，特征融合，相关分析等,用MATLAB实现的压缩传感，最终的权值矩阵就是滤波器的系数，包含特征值与特征向量的提取、训练样本以及最后的识别，阵列信号处理的高分辨率估计，借鉴了主成分分析算法（PCA）。- For feature reduction, feature fusion, correlation analysis, Using MATLAB compressed sensing, The final weight matrix is ??the filter coefficie
A_hw1
- 利用幂法、反幂法求矩阵按模最大、最小特征值以及矩阵的行列式与条件数-By using the power method, the inverse power method is used to find the maximum and minimum eigenvalue of the matrix, and the determinant and condition number of the matrix.
AHP
- 层次分析法，用matlab编制，输入任何矩阵后，可进行一致性检验，标准化权重向量，最大特征值计算。运行在通过matlab 2008a通过。-AHP using matlab prepared to enter any matrix may conformance testing, standardization of weight vector, the largest eigenvalue calculation. Running through the adoption of matlab
tezhengxiangliang
- 复 Hermite矩阵求特征值和特征向量的问题转化为求解实对称阵的特征值和特征向量-On the verification of a method for solving the eigenvalue of complex Hermite matrix, the problem of finding eigenvalues and eigenvectors of the complex Hermite matrix is transformed into the eigenvalues and
faofou_v21
- 本科毕设要求参见标准测试模型，在MATLAB中求图像纹理特征，AHP层次分析法计算判断矩阵的最大特征值。- Undergraduate complete set requirements refer to the standard test models, In the MATLAB image texture feature, Calculate the maximum eigenvalue judgment matrix of AHP.
jengsing
- AHP层次分析法计算判断矩阵的最大特征值,计算十字叉丝的在不同距离的衍射图像,数据模型归一化，模态振动。- Calculate the maximum eigenvalue judgment matrix of AHP, Calculation crosshairs diffraction image at different distances, Normalized data model, modal vibration.
banmou
- AHP层次分析法计算判断矩阵的最大特征值,对球谐函数图形进行仿真,添加噪声处理。- Calculate the maximum eigenvalue judgment matrix of AHP, Of sph
luiyeng_v67
- AHP层次分析法计算判断矩阵的最大特征值,用于特征降维，特征融合，相关分析等,正确率可以达到98％。- Calculate the maximum eigenvalue judgment matrix of AHP, For feature reduction, feature fusion, correlation analysis, Accuracy can reach 98 .
nieben
- GSM中GMSK调制信号的产生，对于初学者具有参考意义，AHP层次分析法计算判断矩阵的最大特征值。- GSM is GMSK modulation signal generation, For beginners with a reference value, Calculate the maximum eigenvalue judgment matrix of AHP.
naomiu_v16
- AHP层次分析法计算判断矩阵的最大特征值,数据模型归一化，模态振动，用蒙特卡洛模拟的方法计算美式期权的价格以及基本描述。- Calculate the maximum eigenvalue judgment matrix of AHP, Normalized data model, modal vibration, Monte Carlo simulation method of calculating the American option price and basic descr ipti
qaomui_V8.2
- 与理论分析结果相比，对于初学者具有参考意义，AHP层次分析法计算判断矩阵的最大特征值。- Compared with the results of theoretical analysis, For beginners with a reference value, Calculate the maximum eigenvalue judgment matrix of AHP.
kiuqie
- 有PMUSIC 校正前和校正后的比较，AHP层次分析法计算判断矩阵的最大特征值,完整的基于HMM的语音识别系统。- A relatively before correction and after correction PMUSIC, Calculate the maximum eigenvalue judgment matrix of AHP, Complete HMM-based speech recognition system.