matlab-examples

Matlab的动态系统建模仿真和分析教程Matlab simulation of dynamic systems modeling and analysis tutorial(Matlab simulation of dynamic systems modeling and analysis tutorial Matlab simulation of dynamic systems modeling and analysis tutorial)

这是用matlab写的RBF网络辨识函数。(it is very important)

一种基于802.11n协议的MIMO-OFDM系统仿真代码。(The simulation code of MIMO-OFDM system based on 802.11n protocol.)

IEEE 802.11ac 协议草案2012年5月版本。正式版本大概是2014年初才能确定。(IEEE 802.11ac draft version of the agreement in May 2012.)

main executing reference usage: usage_overlayColoredBorders_Images.m The objective is to illustrate overlaying of 2 RGB images. Multiple images may be overlay 1 over the other as well.

海洋内波压力波时间序列分析程序。用于分析海洋内波，去除高低平潮。(Ocean wave pressure wave in time series analysis program.Is applied to the analysis of ocean internal waves, get rid of the high and low tide.)

此为模式识别中的Fisher线性判别方法求分类器。进行了男、女错误率和总错误率的统计。 全部程序流程如下： 1、读取FAMALE.TXT文件把身高或体重给数组，并求x1的样本均值m1和内离散度S1； 2、读取MALE.TXT文件把身高或体重给数组，并求x2的样本均值m2和内离散度S2； 3、求最佳变换向量和阀值点； 4、读取Test2.txt文件把对应的身高或体重给数组A并求A的样本数M； 5、把最佳变换向量和阀值点、x代入判别方程g（x）； 6、对本判别的错误率进行统计。(This is the pattern recognition method, to calculate the Fisher linear discriminant classifier .Were male , female and total error rate error rate statistics . All program flow is as follows : 1 , read FAMALE.TXT file to height or weight to the array, and find the sample mean m1 x1 and internal dispersion S1 2 , read MALE.TXT file to height or weight to the array, and find the sample mean m2 and x2 within the dispersion S2 3 , for the best transformation vector and the threshold point 4 , read Test2.txt file to the corresponding height or weight requirements for the array A and A is the number of samples M 5 , the best transformation vector and the threshold point , x substituted into the discriminant equation g (x) 6 , determine the error rate of the statistics . )

matlab的一個教程，ppt可以配合學習的時候使用(the course of matlab7.0,ppt)

《MATLAB从入门到精通》一书中的所有源代码(《MATLAB from entry to the master》source code)

pam算法，能够帮助用户理解pam算法的思想，更好的理解该聚类思想(PAM algorithm, can help users understand the thoughts of PAM algorithm, a better understanding of the clustering thought)