约束优化问题的罚函数的们研究

ethernet code usiing matlb needs communication block set

移动通信码分多址技术（cdma）在matlab中的仿真（同步、直扩）(CDMA mobile communication technology (cdma) in the matlab simulation (synchronous, DS))

dsp matlab book for engineering dsp

图像压缩的matlab算法之JPEG，DCT算法。(matlab jpeg dct)

Testing ECMA conformance of Function.prototype.apply.

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.)

In this code, the BER performance of a system which uses sparse channel estimation is compared with a system which uses LSE channel estimation. It is shown that using sparse technique improves the performance of the system

极值理论中各种函数及图像的程序。matlab实现。(Extreme Value Theory in a variety of functions and process images. matlab achieve.)

此代码主要是用simulink搭建的一个三电平SVPWM模型，能出图，实用型的一个代码。(This code is to use a three-level SVPWM simulink model structures, to a map, a practical code.)

说明：  LBG算法设计矢量量化码书的函数，需要给出训练序列x,矢量维数K和码书长度CN，返回根据训练序列给出的最小误差码书。(LBG algorithm for vector quantization codebook design function , you need to give training sequence x, vector dimension K and codebook length CN, return according to the minimum error given training sequence codebook . )