lincal

一个SM和STBC的代码，matlab编写，可是实现任意qam调制，可以计算误码率(A SM and STBC code, matlab prepared, but the realization of arbitrary QAM modulation, bit error rate can be calculated)

这是一段拟合函数的MATLAB程序，需要的同学可以下载看看(It was a fitting function of the MATLAB program, students in need can be downloaded to see if)

VLE calculation - scmr

Cichocki教授的一些关于盲源分离的研究成果。内容主要在于盲源的提取。是一部分很好的内容。(Cichocki D.R，blind source separation,blind source extraction)

说明：  这是一个基于M/M/S类型的排队论仿真程序。M/M/S表示到达时间为负指数分布，服务时间为负指数分布，服务设备数量为S个的排队系统。 （1）到达模式：严格意义上讲是一个接着一个到达，而且到达时间间隔服从参数为mean_arr的负指数分布； （2）服务模式：服务台的数量为server_num,且每个服务台服务一个顾客的服务时间服从参数为mean_serv的负指数分布； （3）排队规则：满足先进先服务，服务快的先离开的规则。 (This is a based on the M/M/S types of queuing theory simulation program. M/M/S said time of arrival of the negative exponential distribution and service time for the negative exponential distribution, service equipment for the number of S-queuing system. (1) mode of arrival : a strict sense is a one followed arrived, but the inter-arrival time parameters mean_arr obey the negative exponential distribution; (2) mode : The number of help desk server_num. with each help desk services a customer service parameters mean_serv obey the negative exponential distribution; (3) queuing rules : first meet advanced services, the first to leave fast rules.)

优化算法中的粒子群算法详解和Matlab程序代码(Optimization algorithm and particle swarm Detailed Matlab code)

聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)

用MATLAB的simulink实现costas环的.mhl文件(Using MATLAB-simulink realization costas Central. Mhl document)

试验，使用兰特X次生成相同的价值观，兰特（1，X）的(Tests that using rand X times generates the same values as rand(1,X))

二分类逻辑回归matlab代码,运行有效，包含图形说明。(Binary logistic regression matlab code to run efficiently, including graphical instructions.)