matlab-gui

The function applies the Madsen method for Doppler Centroid estimation. The input are: 1) the raw data, the parameter of the distance between samples in azimuth to be correlated and the PRF (Pulse repetition frequency)

实现支持向量回归，能够实现小样本的回归预测分析(Regression analysis to realise the prediction base on small sample)

利用PC上的声卡作A/D采样的MATLAB程序(On the use of PC sound card for A/D sampling of the MATLAB program)

说明：  防碰撞纯扩展ALOHA的matlab仿真算法(Pure ALOHA anti-collision expansion of the simulation algorithm matlab)

matlab实现彩色图像的余弦压缩变换，分阈值不同显示(matlab color image compression cosine transform, threshold different display)

matlab画折线图，通过折线图可以直观显示误差信息。(matlab draw a line chart, line graph can be visualized by the error message.)

Generalised Cross Correlation Algorithms. It is useful to estimate the time delay in almost all situations

四种聚类算法源代码及示例代码,本程序的最终目的是形成一套标准的用于聚类、可扩展的工具。包括的内容有1. 聚类算法：Kmeans和Kmedoid算法、FCMclust, GKclust, GGclust算法 2. 评估分类原型：程序可以在二维图像上绘制出聚类的结果 3. 验证：程序给每一个算法提供验证机制，每个聚类算法会统计Partition Coefficient (PC), Classification Entropy (CE), Partition Index (SC), Separation Index (S), Xie and Beni s Index (XB), Dunn s Index (DI) and Alternative Dunn Index (DII)几种衡量指标。(Four clustering algorithm source code and sample code, the ultimate goal of this process is the formation of a set of criteria for clustering, scalable tool. Including the contents of 1. Clustering Algorithm: Kmeans and Kmedoid algorithm, FCMclust, GKclust, GGclust algorithm 2. Assess the classification of a prototype: The procedure can be two-dimensional images to map out the results of clustering 3. Verify: Program to provide every algorithm authentication mechanism, each clustering algorithm statistics Partition Coefficient (PC), Classification Entropy (CE), Partition Index (SC), Separation Index (S), Xie and Beni s Index (XB), Dunn s Index (DI) and Alternative Dunn Index (DII) of several indicators.)

说明：  实现最陡下降法,研究步长因子和特征扩散度对收敛速度的影响(Achieve the steepest descent method to study the characteristics of step length factor and the proliferation of degree of influence on the convergence rate)

Forecast ARIMA process