calcul

simulink做的BPSK的调制与解调，信号源是随机信号与PN码扩频调制，最后结果与随机信号比较一致(simulink do BPSK modulation and demodulation, the signal source is a random signal with the PN code spread-spectrum modulation, the final result more consistent with the random signal)

说明：  MATLAB与SIMULINK主讲郭毓南京理工大学自动化系 系统的介绍MATLAB与SIMULINK的使用方法(MATLAB and SIMULINK Speaker Guo Yunan Department of Automation, Beijing University of Technology and the SIMULINK system introduced the use of MATLAB)

Example: Modeling an Inverted Pendulum in Simulink

matlab聚类分析源代码，本程序采用的是系统聚类方法。(the programe of matlab )

说明：  近邻传播是一种较新颖的聚类方法，运算量少，聚类速度快。(Affinity Propagation is a new clustering method, which has a low calculation and high clustering speed.)

ant打包所需要的学习资料和相关配置，可以进行学习和本地搭建相关项目进行一切项目的编译和打包(ant but it the function is very good and strong)

说明：  这是一个计算镜头视场的软件，非常简单但是很实用。(This is a calculation of the lens field of view of the software, very simple but very practical.)

说明：  北油通信仿真电子图书。编译环境matlab，全面地介绍了通信仿真的各种方法(North Oil communications simulation of electronic books. Compiler environment matlab, comprehensive introduction to various methods of communication simulation)

matlab simulink implementation and analysis of Three-phase inverter2

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 )