SVM

Three phase high frequency isolated DC-DC converter (published in ELSEVIER)is simulated under symmetrical open loop control. It provides Low voltage conversion ratio, isolation, good regulation with fast dynamic response.

kmeans image segmentation Input: ima: grey color image k: Number of classes Output: mu: vector of class means mask: clasification image mask

基于dds的雷达信号的产生，基于dds的雷达信号的产生(Dds-based radar signal generation)

matlab（第二版) Stephen J. Chapman 著 邢树军 郑碧波译 Matlab 中文论坛首发(A good book for stduy matlab)

仿真数字波束形成(一维数字波束形成仿真和二维数字波束形成仿真)(Simulation of digital beamforming (one-two dimensional digital beamforming simulation))

lfm的MATLAB仿真，可以用于信号分析(lfm of the MATLAB simulation, can be used for signal analysis)

说明：  GAOT工具箱改进，包括主程序ga，种群生成，算术交叉，非均匀变异。(Improve GAOT，includes ga.m，initializega,arithXover,nonUnifMutation)

说明：  matlab 图形图像处理 误差扩散算法(graphic image processing matlab error diffusion algorithm)

特徵粹取（feature extraction）是特徵選取（feature selection）的延伸，簡單地說，我們希望將資料群由高維度的空間中投影到低維度的空間，因此，我們必須找出一組基底向量（base）來進行線性座標轉換，使得轉換後的座標，能夠符合某一些特性。 我們可以將特徵粹取分成「包含類別資訊」和「不包含類別資訊」兩大類。包含類別資訊指的是我們已經知道哪些資料分別歸屬於哪一類；而不包含類別資訊的特徵粹取則適用於我們不知道手上的資料點分別該歸屬於哪一類，甚至連該劃分成幾類都不知道的情況。對於這兩大類資料，可以分述如下： 對於「不包含類別資訊」的資料，我們通常使用「主要分量分析」（principal component analysis），簡稱 PCA。 (Cuiqu features (feature extraction) is feature selection (feature selection) extension, simply put, we want to base the projection data the high-dimensional space to low-dimensional space, so we have to find a set of basis vectors ( base) to the linear coordinate conversion, so that after the coordinate conversion, can meet a number of features. We can feature Cuiqu into " category contains information" and " does not contain a category of information," two categories. Contains categories of information refers to what we already know what kind of information are attributed to Cuiqu features include categories of information are not applicable to information we do not know the points are in the hands of the home in which category, and even that is divided into several class do not know the situation. For these two categories of information, can be divided as follows: For information " does not include the categories of information" , we usually use the " )

应用遗传算法求解循环取货的路径优化问题，可以参考一下。(Application of genetic algorithm optimization cycle paths pickup problem。)