MATLAB-FEM

在MATLAB环境下产生白噪声程序，三个性质可以设定，能够正常使用(In the MATLAB environment to produce white noise process, the three properties can be set to normal use)

boltzman implement with matlab

线性方程组，非线性方程组，最小二乘拟合等数值算法的matlab实现。(Linear equations, nonlinear equations, numerical algorithm such as least-squares fitting of the realization of matlab.)

sin 函数功能，用matlab，比较常用，大家可以做个参考(function of sin by matlab)

windows控制台下的一个小字典程序,可以自己添加字典,使用.(A small dictionary program under windows console, you can add your own dictionary to use.)

无线与移动通信中MIMO-OFDM系统2*1天线选择的matlab仿真与实现(Matlab simulation and implementation of MIMO-OFDM System 2* 1 antenna selection in wireless communication and mobile)

matlab编写的潮流计算程序，对于c语言头痛者的良好选择(matlab prepared flow calculation procedures, for c language are a good selection headache)

说明：  hurst指数估计 This function does dispersional analysis on a data series, then does a Matlab polyfit to a log-log plot to estimate the Hurst exponent of the series. This algorithm is far faster than a full-blown implementation of Hurst s algorithm. I got the idea from a 2000 PhD dissertation by Hendrik J Blok, and I make no guarantees whatsoever about the rigor of this approach or the accuracy of results. Use it at your own risk. (hurst This function does dispersional analysis on a data series, then does a Matlab polyfit to a log-log plot to estimate the Hurst exponent of the series. This algorithm is far faster than a full-blown implementation of Hurst s algorithm. I got the idea from a 2000 PhD dissertation by Hendrik J Blok, and I make no guarantees whatsoever about the rigor of this approach or the accuracy of results. Use it at your own risk. )

状态估计的gui,运用matpower,实现状态估计，里面还调用多个自己编的程序(Gui of State estimation)

介绍一个稳健性分析工具箱。主要做稳健性主成分、主成分回归、分类。(Our toolbox currently contains implementations of robust methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST-LTS, MCD-regression), principal component analysis (RAPCA, ROBPCA), principal component regression (RPCR), partial least squares (RSIMPLS) and classification (RDA). Only a few of these methods will be highlighted in this paper. The toolbox also provides many graphical tools to detect and classify the outliers. The use of these features will be explained and demonstrated through the analysis of some real data sets.)