GDFNN

周立功公司提供的《51单片机新技术》 (Ligong weeks provided by the company )

连续小波变换的MATLAB程序代码（morlet小波），主要用于信号分析(morlet wavelet)

将matlab应用于基于纹理特征的遥感影像分类中的标准差分类(Matlab applied to the differential classification based on texture features of the remote sensing image classification standard)

4步标定法，主要是实现计算机数字图像处理摄像机的标定，操作简单，比较实用(4-step calibration method is to achieve a computer digital image processing camera calibration, easy operation, more practical)

菜单的仿真，通过修改菜单可以修改自己需要修改的参数，例如PID(Simulation menu, the menu can be modified by modifying the need to modify the parameters of their own, such as PID)

RNNSIM v.2 package contains a number of m-files for training and evaluation of the random neural network. All functions have been thoroughly tested. After downloading the separate files or the zipped file, make sure that they are stored or extracted in the directory /rnnsimv2 Below an overview of the files contained in this directory along with a brief description of what they do. The on-line help facility explains how to call the different functions. You simply write help <function-name> in the MATLAB command window. Along with the m-files in this directory you will find a manual for the simulator in PDF format (rnnsimv2.pdf). Start by printing this out and read the release notes. Two simple demonstration programs are given to illustrate how most of the functions work.

用有限元方法求解一维边值问题的matlab程序(With one-dimensional finite element method for solving boundary value problems of the matlab program)

Fountain code implementation over ersaure channel

判別分析法（discriminant analysis），是在已知的分類之下，一旦遇到有新的樣本時，可以利用此法選定一判別標準，以判定如何該將新樣本放置於那個族群中。 (Discriminant analysis (discriminant analysis), under the classification of the known event of the new sample, the method has been applied to select a criterion to determine how the new sample is placed in that population.)

支持向量机(SVM)用于分类的算法实现 function [D, a_star] = SVM(train_features, train_targets, params, region)(Support Vector Machine (SVM) algorithm for classification in function [D, a_star] = SVM (train_features, train_targets, params, region))