NEMD_Supplement

SVM分类器，用于对多维采样点进行分类。可根据类别数修改分类器，我们的模式识别作业。(SVM classifier, multi-dimensional sampling points used for classification. Can be modified according to the number of classification categories, and our pattern recognition operation.)

基于matlab的电力系系统潮流计算典例完整源代码。 (Matlab power system based on power flow callculation example of a complete source code. )

convolutional coding and Viterbi decoding using matlab

wave guide on diferent imax

非负矩阵分解，测试cbcl,orl,swimmer数据集程序，画图程序(NMF, test cbcl, orl, swimmer data collection program, drawing program)

High Speed Face Recognition Based on Discrete Cosine Transforms and Neural Networks

图像直方图的绘制，此程序提供两种方法，一是利用matlab自带得工具箱绘制，另一种是利用统计学的方法编程实现，最后对两种方法进行比较(Image histogram drawing, this program provides two methods, one using matlab toolbox comes too draw, another is the use of statistical methods to program implementation, and finally to compare the two methods)

样本熵 结合样本熵和谐波小波分解，MATLAB程序原代码(Sample entropy and sample entropy combined with harmonic wavelet decomposition, MATLAB program source code)

做过模糊PID的朋友很多遇到过这种问题，就是仿真启动不起来，停留在e-7等地方或者仿真很慢，自次上次有个朋友说改变步长能解决这个问题，最后的曲线能达到满足要求的时候，我让他找个例子他就没有后文，以前我做了很多这样的模糊PID，但靠改变步长并没有解决问题，仿真曲线直接发散出去了，我自己这两天也总在思考这个问题，通过不断的调试总结，明白了不少，给遇到这个问题的朋友讲解一下解决办法吧，以一个例子详解：(Done a lot of friends of the fuzzy PID encountered this problem, simulation start up, stay in the e-7 and other local or simulation is very slow, have a friend who said to change the step size to solve this problem since the times the last, the finalcurve to achieve to meet the requirements when I asked him to find examples of his later, I have done a lot of fuzzy PID, but by changing the step does not solve the problem, the simulation curve directly divergence out, Itwo days always thinking about this problem, summarized by the debug and understand a lot of friends to experience this problem to explain the solution, an example Detailed:)

聚类树的matlab软件包。层次聚类分析器。(ClusTree is a GUI Matlab tool that: Enables an easy and intuitive way to cluster, analyze and compare some hierarchical clustering methods Consists of a two-step wizard that wraps some basic Matlab clustering methods and introduces the Top-Down Quantum Clustering algorithm. Provides a flexible and customizable interface for clustering data with high dimensionality. Allows both textual and graphical display for the clustering results. Can either apply hierarchical clustering to a dataset or analyze ‘pre-clustered’ results. )