td-Systeme-en-BF--Marges-de-stabilite-Precision

遗传算法Matlab源代码,优化目标函数比较广泛，是常用的算法之一，同时也是学习其他算法的基础。(Genetic algorithm (ga) )

recursive least square algorithm

这是一个已知ar模型参数求功率谱密度的函数(Ar model parameters are known to seek the power spectral density function)

说明：  一个用于计算精馏塔理论塔板的小程序,可以实现图示化.(a theory used in the calculation of distillation tray of small procedures, can achieve Graphic.)

快速高斯滤波工具箱（fast guassian toolbox），具有良好的滤波效果，希望大家可以用来进行探讨。(Fast Gaussian filter Toolbox (fast guassian toolbox). good filtering effect, we hope can be used to explore.)

模拟幅度调制系统 AM调制与相干解调 DSB调制与相干解调(DSB modulation and coherent demodulation of AM modulation and coherent demodulation of analog amplitude modulation system)

This project compares the performance of SOM versus LVQ in classification problems. Given two data sets: ‘iris.dat’ has 150 patterns of 3 classes with 4 features. ‘wine.dat’ has 178 patterns of 3 classes with 13 features. For SOM, use its algorithm (not use MATLAB tool), but for LVQ use MATLAB tool.

Welch法功率谱密度估计 改进的平均周期图法来求取随机信号功率谱密度估计的Welch法。(Welch method of power spectral density estimates to improve the average periodogram to strike a random signal power spectral density estimation method of Welch.)

这个程序是3机9节点潮流计算的程序，是电力系统编程的入门程序(power flow machine 3)

％ EM algorithm for k multidimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％( EM algorithm for k multidimensional Gaussian mixture estimation Inputs: X (n, d)- input data, n = number of observations, d = dimension of variable k- maximum number of Gaussian components allowed ltol- percentage of the log likelihood difference between 2 iterations ([] for none) maxiter- maximum number of iteration allowed ([] for none) pflag- 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) Init- structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) Ouputs: W (1, k)- estimated weights of GM M (d, k)- estimated mean vectors of GM V (d, d, k)- estimated covariance matrices of GM L- log likelihood of estimates)