Speech-Assignment-No5

MATLAB初学者 MATLAB初学者 包好了一些用matlab做无约束规划的源程序(MATLAB beginner beginner package MATLAB with matlab to do some good non-constrained programming source code)

This function obtains the pi model of a transmission line the ABCD constants of the line.

波束形成Matlab程序，通过matlab程序，了解波束成形的算法过程，以及其含义(Beamforming Matlab program by matlab program to understand the process of beamforming algorithm, and its meaning)

光纤无线通信系统中传输性能的研究，用于图像处理的独立分量分析，感应双馈发电机系统的仿真。( Fiber Transmission wireless communication system performance, Independent component analysis for image processing, Simulation of doubly fed induction generator system.)

说明：  MATLAB 粒子群单目标优化程序，简单易懂！(Particle swarm optimization of single objective optimization program, easy to understand! )

说明：  经典的music算法，用matlab实现(music in matlab)

关于MIMO OFDM不同发射和接收的天线数目下的系统容量的仿真(Firing as for MIMO the OFDM dissimilarity and under the recipient number of aerials of the imitating of system capacity is really)

卡尔曼滤波中CA模型下匀速运动与匀加速运动matlab仿真(Uniform motion and uniformly accelerated motion in the CA model in the Kalman filter matlab simulation)

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 )

有源滤波器控制的一个example，供大家参考学习(Active filter control of a example, for your reference learning)