04720961

基于肤色的人脸识别代码（转自MATLAB论坛）(Face recognition based on color code (from MATLAB Forum))

数字带通滤波器设计原理以及设计不同带宽的带通滤波器实例(Digital band-pass filter design theory and design of different instances of band-pass filter bandwidth)

Perform a Jarque and bera test and return test statistique

Matched filter creator for matlab

给文件包含了几乎所有数学建模用到的分析法，如模糊数学，神经网络，灰色模型，并包含有相关模型matlab代码，可作为建模竞赛培训材料。(To the file contains almost all of the mathematical modeling used in the analysis, such as fuzzy, neural network, the gray model and includes the relevant model matlab code can be used as training materials modeling contest.)

用Fourier变换求信号的功率谱(分段周期图法)(Calculate the power spectrum of signal using Fourier transform method)

matlab配网33节点用牛顿拉夫舜法求解电网潮流计算。(matlab distribution network node 33 Ralph Shun calculated using Newton method for solving power flow.)

我们将通过MATLAB仿真在发送平均功率和传输速率相同的情况下，在传输特性为理想的平的和非平(即存在码间干扰)的信道上，对多载波OFDM和单载波QPSK的误码率进行比较。(We will send the average power by MATLAB simulation and the transmission rate in the same circumstances, in the transmission characteristics of the ideal of peace and non-flat (as is the inter-symbol interference) of the channel, multi-carrier OFDM and single carrier QPSK BER rates were compared.)

光纤光栅受压力仿真，改变反射回来的波长光纤光栅受压力仿真，改变反射回来的波长(Fiber Bragg grating simulation by the pressure to change the wavelength of reflected)

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)