mutualerror-and-snapts

source code for finding histogram of an image and inset plotting in matlab

Matlab程序设计与应用习题精选！练习完这些习题后，对matlab的掌握会得到很大的提高。(Selection the Matlab program design and application exercises! Practice these exercises, mastery of matlab will be greatly improved.)

Adam bash forth predictor and corrector method.

分数阶傅里叶变换 Matlab仿真实现 单分量的LFM信号的检测和参数估计( Fractional Fourier Transform Matlab simulation to achieve a single-component LFM signal detection and parameter estimation)

MUNK剖面传播损失仿真，数据验证在TXT文件中，希望有所帮助。(MUNK sectional propagation loss simulation, data validation TXT file, hope that helps.)

均值滤波（线性平滑滤波）和中值滤波程序·······(Average value filter (linear smoothing filter) and value filter )

power_transfohyst.rar,功率电子领域matlab仿真文件，已经验证过，程序运行正常(power_transfohyst.rar,Power electronics field matlab simulation file, has already been verified, the normal operating procedures)

海杂波K-分布模型模拟仿真程序 海杂波K-分布模型模拟仿真程序(K distribution in matlab )

说明：  本程序用矩阵实现有限差分算法，本程序求解第一类边界条件下无源矩形金属槽电位分布，计算电磁学。(A simple code for finite difference method.)

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)