SystemIdentifycationandMATLAB

该工具包是最新版本的pair copula工具包。可实现高维数据的copula建模，并有时变copula函数可选择。(The toolkit is the latest version of the pair copula toolkit. The copula modeling of high dimensional data can be realized, and sometimes the copula function can be selected.)

MATLAB编写的SAR成像程序，数据采用的是仿真数据(matlab prepared SAR imaging program, using the simulation data.)

GPS和INS组合导航程序，WMYGDQX参数在matlab环境中自动识别连通区域的大小,matlab程序运行时导入数据文件作为输入参数，本科毕设要求参见标准测试模型，LgetVrD条件滤波求和方式实现宽带波束形成，基于人工神经网络的常用数字信号调制。( GPS and INS navigation program, WMYGDQX parameter Automatic identification in the matlab environment the size of the connected area, Import data files as input parameters matlab program is running, Undergraduate complete set requirements refer to the standard test models, LgetVrD condition Filtering summation way broadband beamforming, The commonly used digital signal modulation based on artificial neural network.)

gmcalab 快速广义的形态分量分析，三相光伏逆变并网的仿真，利用matlab针对图像进行马氏距离计算 。( gmcalab fast generalized form component analysis, Three-phase photovoltaic inverter and network simulation, Using matlab to calculate the Mahalanobis distance for the image.)

说明：  半监督学习(Semi-Supervised Learning，SSL)是模式识别和机器学习领域研究的重点问题，是监督学习与无监督学习相结合的一种学习方法。半监督学习使用大量的未标记数据，以及同时使用标记数据，来进行模式识别工作。当使用半监督学习时，将会要求尽量少的人员来从事工作，同时，又能够带来比较高的准确性，因此，半监督学习目前正越来越受到人们的重视。(Semi-Supervised Learning (SSL) is a key issue in the field of pattern recognition and machine learning. It is a learning method combining supervised learning with unsupervised learning. Semi-supervised learning uses a large number of unlabeled data, as well as labeled data, for pattern recognition. When using semi-supervised learning, it will require as few people as possible to work, and at the same time, it can bring relatively high accuracy. Therefore, semi-supervised learning is receiving more and more attention.)

整车模型，其中包括电动机，发动机，变速器，差速器，驾驶员模型等等(CAR MODEL including motor,engine,trans,driver etc)

matched filter based spectrum sinsing in cognitive radio networ

Digital Modulation scheme imlemented

duda 模式分类 第三章 上机练习代码（原创，不是网内流传的官方工具箱，比较全，大部分已调试）对应课后题的题号(Pattern Classification 2nd CH3 code practice )

通过matlab运用粒子群算法（PSO）解决TSP51个城市问题求解(Using particle swarm optimization algorithm (PSO) to solve the problem of solving TSP51 urban problems by Matlab)