IP-6(28_02_2012)

这里实现了基于四种SVM工具箱的分类与回归算法: 1、工具箱：LS_SVMlab Classification_LS_SVMlab.m - 多类分类 Regression_LS_SVMlab.m - 函数拟合 2、工具箱：OSU_SVM3.00 Classification_OSU_SVM.m - 多类分类 3、工具箱：stprtoolsvm Classification_stprtool.m - 多类分类 4、工具箱：SVM_SteveGunn Classification_SVM_SteveGunn.m - 二类分类 Regression_SVM_SteveGunn.m - 函数拟合 更详细的相关函数说明请通过help命令查看！(Here the realization of the four SVM toolbox based on the classification and regression algorithm: 1, Toolbox: LS_SVMlabClassification_LS_SVMlab.m- Multiclass Classification Regression_LS_SVMlab.m- function fitting 2, the toolbox: OSU_SVM3.00Classification_OSU_SVM.m- Multiclass Classification 3, Toolbox: stprtoolsvmClassification_stprtool.m- Multiclass Classification 4 toolbox: SVM_SteveGunnClassification_SVM_SteveGunn.m- II Category Regression_SVM_SteveGunn.m- function fitting a more detailed explanation of the correlation function through the help command to view!)

说明：  恒虚警检测CFAR仿真，杂波边缘 多目标情况(the coding of the CFAR technology)

说明：  通信原理课本中的一些实例代码，方便大家使用(Communication principles in the textbook of some examples of code, easy to use)

matlab模拟仿真电场的分布变化，用FDTD算法(matlab simulate electromagnetic field)

很详细地介绍了matlab在图像处理中的应用，其中有车牌号的识别，染色体的识别等，还有文档说明，精心整理的，希望和大家分享！(Very detailed description of the matlab in image processing applications, including license plate number recognition, chromosome identification, as well as documentation and careful finishing, and would like to share!)

说明：  空间电压矢量仿真模型和仿真论文 空间矢量PWM的SIMULINK仿真(SVPWM simulation model and simulation study)

可以对数量少、残缺、不完整的数据进行预测，如人口、缺水量等。也可以对灾害发生时间进行预测。(Can be a small number, mutilated, incomplete data to predict, such as population, water shortage and so on. You can also predict the time of occurrence of the disaster.)

K最邻近密度估计技术是一种分类方法，不是聚类方法。 不是最优方法，实践中比较流行。 通俗但不一定易懂的规则是： 1.计算待分类数据和不同类中每一个数据的距离（欧氏或马氏）。 2.选出最小的前K数据个距离，这里用到选择排序法。 3.对比这前K个距离，找出K个数据中包含最多的是那个类的数据，即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method. It is not the best method, but it is popular in practice. Popular but not necessarily understandable rule is: 1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov). 2. select the minimum distance from the previous K data, where the choice sorting method is used. 3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)

说明：  ADPCM的matlab程序，包括编码和解码两部分，标准G。721编解码。(ADPCM the matlab program, including the encoding and decoding of two parts, the standard G. 721 codec.)

IEEE30 bus 系统的matpower格式的数据格式(IEEE30 bus system matpower format data format)