gmcalab_le1.0

说明：  实验2 离散信号的DFT的相关matlab 算法以及产生方法 有效的实现FFT功能(Experiment 2 discrete signals related to matlab DFT algorithm and the method of effective realization of FFT function)

经典的基本PSO粒子群优化算法的测试函数，包括主程序和测试函数，非常经典(The classic PSO basic particle swarm optimization test functions, including the main program and test functions, very classic)

2DPSK的链路仿真，信号源的产生，PCM编码，调制解调以及加燥 (2 dpsk link simulation signal source, PCM coding and modulation demodulation and dry)

MIMO技术详细技术 比较经典的讲解，比较经典(MIMO technology to explain detailed technical more classic)

Prony分析是获取系统振荡模式特征的一种非常有效的方法，它可以通过给定输入信号下的响应直接估计系统的振荡频率、衰减、幅值和初相位。本文基于Prony算法提出一种用振荡模式能量级鉴别电力系统大干扰下主导低频振荡模式的方法。(Prony analysis acquisition system oscillation mode features a very effective way, It can set the input signal to the estimated direct response to the oscillation frequency, attenuation, amplitude and phase. Based on Prony algorithm proposed by the energy level oscillation mode differential interference large power systems under low-frequency oscillation mode lead style approach.)

最小冗余最大相关性（MRMR）（MRMR.M） 需要外部库。详情请见MRMR。下载一个更新版本的互信息工具箱 偏最小二乘（PLS）回归系数（ReGCOEF.m） 使用MATLAB统计工具箱中的PLSReress ReliefF（分类）和RReliefF（回归）（ReleFracePr.M.） 从Matlab STATS工具箱中包装Releff.m。这是Matlab R2010B以后提供的。 ReliefF的另一个选择是使用ASU特征选择工具箱中的代码。这使用WEKA工具箱的ReleFEF，因此需要额外的库。请参阅相应的文档。 费雪评分（Fisher评分） 围绕ASFS特征选择工具箱围绕FSFisher。M(Minimum Redundancy Maximum Relevance (mRMR) (mRMR.m) Needs external library. See mRMR.m for details. Download a newer version of the mutual information toolbox Partial Least Squares (PLS) regression coefficients (regCoef.m) Uses plsregress.m from MATLAB statistics toolbox ReliefF (classification) and RReliefF (regression) (relieffWrapper.m) Wraps around relieff.m from the MATLAB stats toolbox. This is available MATLAB r2010b onwards. Another option for ReliefF is to use the code from ASU Feature Selection toolbox. This uses ReliefF from weka toolbox and hence needs additional libraries. Please see the corresponding documentation. Fisher Score (fisherScore.m) Wraps around fsFisher.m from the ASU Feature Selection toolbox)

Simulation model for solving the load flow problem Two area method

用matlab写的模式识别中C均值分类算法，数据被分成2类，经验证正确(Pattern recognition with matlab written in C-means classification algorithm, the data is divided into two categories, proven correct)

Matlab LAWS能量滤波函数 主要用于纹理的分析(Matlab LAWS energy filtering function is mainly used for texture analysis)

Used function P(x) = 1+x− x2+0.2x3+0.1x4 and its known the values P(x)=[ -6.2 -8.3 -5.0 -1.1 1 1.3 2.2 8.5 27.4], in x= [-4 -3 -2 -1 0 1 2 3 4] to interpolate in the xi = − 4 + 0.1i, i = 0, 1, 2, . . . 80. Results are graphically compared!(Used function P(x) = 1+x− x2+0.2x3+0.1x4 and its known the values P(x)=[-6.2-8.3-5.0-1.1 1 1.3 2.2 8.5 27.4], in x= [-4-3-2-1 0 1 2 3 4] to interpolate in the xi = − 4+ 0.1i, i = 0, 1, 2, . . . 80. Results are graphically compared!)