folder-3

Diskstra最短路径算法的c++源程序，以及编译好的matlab动态链接库，学习留形学习算法及程序必备。(Diskstra shortest path algorithm c++ source code, and compiled matlab dynamic link library, learning algorithms and procedures remain an essential form of learning.)

关于高精度加法运算的无误差变换matlab算法程序。(Accurate floating point sum programs)

SVM主程序,专门用来调用SVM程序的,主要是先建模再验证，得出较好的分类器。(SVMmain program，for using SVM to classify)

跳频通信的仿真 用Matlab实现 跳频的图案(Simulation of frequency hopping communication to achieve frequency hopping pattern with Matlab)

matlab manual MATLAB is an interactive system for doing numerical computations. MATLAB makes use of highly respected algorithms and hence you can be confident about your results

针对ARMA的仿真源程序 对P, Q阶的求解(ARMA source for the simulation of P, Q-order solution)

二自由度悬架系统在MATLAB/SIMULIN中建立模型，可以的到不同输入下所需的输出(Two degrees suspension system model in the MATLAB/SIMULIN, can to the desired output under different input)

获取信号的强弱，非常好用，欢迎大家下载，好了不说了(Get signal strength, very easy to use, welcome to download, well, do not say)

LMS算法的matlab实现，用于智能天线以及波束成形()

基于奇异值分解的人脸识别方法 梁毅雄 龚卫国 潘英俊 李伟红 刘嘉敏 张红梅 提出了一种将傅里叶变换和奇异值分解相结合的人脸自动识别方法.首先对人脸图像进行傅里叶变换,得到其具有位移不变特性的振幅谱表征.其次,从所有训练图像样本的振幅谱表征中给定标准脸并对其进行奇异值分解,求出标准特征矩阵,再将人脸的振幅谱表征投影到标准特征矩阵后得到的投影系数作为该人脸的模式特征.然后,对经典的最近邻分类器算法进行了改进,并采用模式特征之间的欧式距离作为相似性度量,从而完成对未知人脸的识别.采用ORL (Olivetti Research Laboratory)人脸库对本文提出的人脸识别方法进行验证,获得了100.00 的识别率.实验结果表明,本方法优于现有的基于奇异值分解的人脸识别方法,且对表情、姿态变换等具有一定的鲁棒性. (Face recognition based on singular value decomposition method Deliberate simultaneously Gong Weiguo Li Wei Hung Stephen Lau, Hong-Mei Zhang Ying-Jun Pan Paper, a Fourier transform and singular value decomposition of the combination of automatic face recognition. First of all, the face image by Fourier transformation, it has the same characteristics of the displacement amplitude spectra. Secondly, all training The amplitude spectrum of the sample images given in standard face representation and its singular value decomposition, find the standard characteristic matrix, then the amplitude of spectral characterization of human faces projected onto the standard characteristic matrix of projection coefficients obtained as the face of the model features . Then, the classical nearest neighbor classifier is improved, and the use of Euclidean distance between pattern features as the similarity measure, thus completing the identification of unknown human faces. using ORL (Olivetti Research La)