UGM

层次分析法的计算,试用前要充分理解该方法的使用规则(The calculation of the analytic hierarchy process)

this file is matlab code for hunting search algorithm.i think it can be so useful.thanks

基于MATLAB编写的代码，采用小波阈值降噪的方法对采集的光谱信号进行滤波处理。可自行设定阈值，实现不同的滤波效果。(Based on the code written in MATLAB, using wavelet threshold noise reduction method for filtering spectrum signal acquisition process. You can set the threshold to achieve different filter effects.)

用户界面（或接口）是指：人与机器（或程序）之间交互作用的工具和方法。如键盘、 鼠标、跟踪球、话筒都可成为与计算机交换信息的接口。 图形用户界面（Graphical User Interfaces ，GUI）则是由窗口、光标、按键、菜单、文 字说明等对象（Objects）构成的一个用户界面 (User interface (or interface) refers to: Human and machine (or process) the interaction between the tools and methods. Such as keyboard, mouse, track ball, microphone and computer can become the interface to exchange information. Graphical user interface (Graphical User Interfaces, GUI) is by the window, the cursor, buttons, menus, text and other objects (Objects) consisting of a user interface)

卷积编码和viterbi译码程序，绝对可用，完全中文注释(none)

非常好的MATLAB教程，当你看完教程后你会发现MATLAB比其它开发语言要简单的多(MATLAB tutorial is very good, when you read the tutorial you will find MATLAB simpler than other development languages ​ ​ to)

7-4线性分组码源代码在matlab中的程序实现，矩阵，信息为和校验位(7-4 linear block code source code in the matlab program implementation, matrix, for information and check digit)

ekf与UKF算法的MATLAB源代码,其中的例子是以stewart六自由度并联机构展开的。(The matlab codes about extended kalman filter and unscented kalman filter， and the demo is about 6 degree of freedom stewart parallel manipulator)

虚拟激励法的实现，4自由度的随机振动问题，包含详细说明，对出学者有很好启示作用！(The realization of the virtual excitation method, 4 degrees of freedom random vibration problems, including details of the scholars have a good inspiration!)

介绍一个稳健性分析工具箱。主要做稳健性主成分、主成分回归、分类。(Our toolbox currently contains implementations of robust methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST-LTS, MCD-regression), principal component analysis (RAPCA, ROBPCA), principal component regression (RPCR), partial least squares (RSIMPLS) and classification (RDA). Only a few of these methods will be highlighted in this paper. The toolbox also provides many graphical tools to detect and classify the outliers. The use of these features will be explained and demonstrated through the analysis of some real data sets.)