Three_phase_PV_inverter

jugal beam analysis m file in matlab

MATLAB® is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numeric computation. Using the MATLAB product, you can solve technical computing problems faster than with traditional programming languages, such as C, C++, and Fortran.You can use MATLAB in a wide range of applications, including signal and image processing, communications, control design, test and measurement, financial modeling and analysis, and computational biology. Add-on toolboxes (collections of special-purpose MATLAB functions, available separately) extend the MATLAB environment to solve particular classes of problems in these application areas.MATLAB provides a number of features for documenting and sharing your work. You can integrate your MATLAB code with other languages and applications, and distribute your MATLAB algorithms and applications.

好用的matlab实现凸优化工具包，适用于压缩感知重构过程(Easy to use matlab to achieve a convex optimization tool kit for compressed sensing reconstruction process)

本文从matlab的基本知识入手，本书侧重于利用大量的实例来引导读者快速掌握matlab的各种功能。(In this paper, the basic knowledge to start matlab, the book focuses on the use of a large number of examples to guide readers to quickly grasp the various features of matlab.)

％ 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)

说明：  ANN-BP分类器设计，用其中一半的数据采用ANN-BP算法设计分类器，另一半数据用于测试分类器性能。(ANN-BP classifier design, with half of the data using ANN-BP algorithm design classifier, the other half of the data used to test the classifier performance.)

parameter dh denavit

讲解matlab环境下电力系统模块的组成、使用方法、经典案例。(Matlab environment to explain the composition of the power system module, use, classic case.)

通信原理，matlab编程 文档 源代码(code compile)

按照能耗最优原则对机器生产时间用遗传算法解决(Optimal energy consumption in accordance with the principles of machine production time by using genetic algorithms to solve)