cauchy

training for dvr with nn control

递推最小二乘程序，是进行系统辨识很好的程序(Recursive least-squares procedure, is a good program for system identification)

canny detector code which performs extracting edges from a image and gives efficient results compared to other methods

演示了mallat算法的实现过程，包括分解与重构，原始波形用的是两个不同频率的正弦函数(Mallat algorithm demonstrated the realization of the process, including the decomposition and reconstruction, with the original waveform are two different frequency sine function)

说明：  用vb写的遗传算法程序。是一些通用代码，与具体问题对应的代码空出来，用的时候填进去。(used to write the genetic algorithm. Some common code, with the specific issues the corresponding code space, and the time spent filling into account.)

电力系统IEEE118节点系统潮流计算所需数据(Data needed for power flow calculation of power system IEEE118 test system)

说明：  这里面介绍了很多关与先进PID控制及其MATLAB仿真(This introduced a lot of inside clearance and advanced PID control and MATLAB simulation)

gmsk modulation of numerical data generated by a random source, the output of the modulator give two signals, the first is gmsk signal and the other is gaussien frequency pulse shape

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)

matlab基于最小错误率的贝叶斯分类器设计(Bayesian classifier design matlab minimum error rate)