Commodity-One-Factor-Spot-Model

Solves oxy combustion of methane in matlab

介绍了一种单频的GPS软件接收机的设计，其matlab代码很有参考价值(introduce a design of a single frequency GPS software receiver-attached matlab)

基本混沌系统的同步程序，用matlab编写，并且在其他系统中也是通用的。(chaos system is made by matlab language,which is suitable for other system.)

MATLAB code that implements the generalized delta rule

运筹学里的优化算法，源代码，而且经过matlab运行过了(Operations Research in the optimization algorithm, source code, and after a run Matlab)

较详细的介绍了Matlab通用工具箱函数的用法，对掌握matlab工具箱函数的查询与定位有帮助。(More detailed introduction of the Matlab toolbox functions common usage, to grasp the matlab toolbox function of the inquiries related to positioning has helped.)

说明：  基于欧氏距离求解带状区域二值图的目标区域宽度(Solving Euclidean distance-based binary image of the band region the width of the target area)

matlab 批量读DBF文件，非常好用，需要下载一个dbfread函数文件(matlab batch read DBF files, very easy to use, need to download a dbfread function file)

微芯光伏微逆变器系统的matlab仿真文件(Microchip photovoltaic micro-inverter system matlab simulation files)

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)