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lagrange.m proram with matlab

较为精确的逆变器仿真模型，包括控制系统，可作为初学者的重要参考,很有参考价值(More accurate simulation model of inverter, including the control system can be used as important reference for beginners，a good reference)

数字PID的matlab仿真程序，针对刘金琨的先进PID控制的matlab仿真，里面的部分程序进行了修改(Digital PID of matlab simulation program for Liu Jinkun matlab advanced PID control simulation, which part of the program were modified)

基于MATLAB的pso算法的测试函数Schwefel函数(Test function Schwefel function of PSO algorithm based on MATLAB)

利用Matlab编写的遗传算法工具箱，可应用于不同的算法设计中(Prepared by use of Matlab genetic algorithm toolbox, the algorithm can be applied to different design)

16 qadrature amplitude modulation convert 4bits to its reall and imajenary points

gives the initial and final velocities of a trajectory connecting R0 to Rf over a transfer time of dT. This method uses universal variables, from Vallado.

QPSKtiaozhi的一种方式，采用包给了白噪声，加入了两路分别处理条制服的方式(QPSKtiaozhi a way, using the packet to the white noise, to join the two handle pieces uniform)

这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。 全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。 调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。 调用最小风险贝叶斯分类器决策子函数时，根据先验概率，再根据自行给出的5*5的决策表，通过比较概率大小判断一个体重身高二维向量代表的人是男是女，放入决策数组中。 主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小风险贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到在一定先验概率条件下，决策表中不同决策的错误率的统计。 (This is a pattern recognition classifier minimum risk Bayes design .In reference to the case of routine , self- improvement in a certain a priori probability conditions, male , female and total error rate error rate statistics , into which each array . All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions . Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector The third step is the application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function . Bayesian classifier )

说明：  matlab种各种随即数的生成源代码，文件中有近20种随机数的产生。(the number of species then matlab generate all the source code, file, nearly 20 kinds of random number generation.)