jiandanls-svm

kalman filter toolbox for matlab...

matlab完美实现ICP（迭代最近点算法）算法，功能强大，适合匹配数据点(matlab perfect realization of ICP (iterative closest point algorithm) algorithm, a powerful, data points for matching)

Take an image window and place it in a squared window such that it s size is maximized without distortion.

this is matlab code to view morping

错误隐藏matlab 代码，很不错的pocs(error concealment)

Window下c程序设计的初级教程（含代码），c程序员在windows下开发程序应该阅读的第一部资料(Programming windows via C)

ETSI STANDDARD TEST DOCUMENT FOR TBR6

说明：  珍藏版，可实现，新K均值聚类算法，分为如下几个步骤： 一、初始化聚类中心 1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。 2、用前C个样本作为初始聚类中心。 3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。 二、初始聚类 1、按就近原则将样本归入各聚类中心所代表的类中。 2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，重复操作，直至所有样本归入相应类中。 三、判断聚类是否合理 采用误差平方和准则函数判断聚类是否合理，不合理则修改分类。循环进行判断、修改直至达到算法终止条件。(NewK-means clustering algorithm ，Divided into the following several steps: A, initialize clustering center 1, according to the specific problems, from samples with experience selected C a more appropriate focus the sample as the initial clustering center. 2, with former C a sample as the initial clustering center. 3, will all samples randomly divided into C, calculate the sample mean, each the sample mean as the initial clustering center. Second, initial clustering 1, according to the sample into the nearest principle clustering center represents the class. 2, as this, take the its recent as clustering center of that category, recount the sample mean, update clustering center. And then taking off, as this, repeated operation until all samples into the corresponding class. Three, judge clustering is reasonable Adopt error squares principles function cluster analysis.after clustering whether reasonable, no reasonable criterion revisio)

This is the description file for the matlab .m file with the same name wlstotal in spanish language.

此Matlab程序是用于齿轮故障检测，其中包含时域和频域分析等。(This is a Matlab program for the detection gear failure, which includes the time domain and frequency domain analysis.)