Matlab7.0

Computes the Discrete Fourier Transform of a function.

使用分解聚类算法在IRIS数据上进行聚类分析，IRIS数据是由鸢尾属植物的三种单独的花的测量结果所组成，模式类别数为3，特征维数是4，每类各有50个模式样本，总共有150个样本。(The use of decomposition in the IRIS data clustering algorithm on the cluster analysis, IRIS data are from the iris flower three separate components of the measurement results, models for category 3, 4 are characteristic dimension, of each type of each 50 model samples, a total of 150 samples.)

一个matlab time dot main cross-correlation， 解压后在matlab里运行即可，可计算各种数字信号的延迟，没有密码(a Matlab time dot main cross-correlation , unpacked were operating in Matlab can, translate various digital signal delays, no password)

数学建模算法介绍以及在Matlab上的实现(Mathematical modeling algorithm implementation in Matlab on)

本程序是用经验模态分解(EMD)算法检测VF信号的，该算法的灵敏度和特异性都达到了90 以上。(This procedure is used empirical mode decomposition (EMD) algorithm to detect VF signals, the sensitivity and specificity of the algorithm has reached more than 90 .)

说明：  小波包分解程序，小波基函数db5进行三层分解(Wavelet packet decomposition program, wavelet basis function db5 for three-level decomposition)

最速下降法，搜索算法，匈牙利算法。等等···(Steepest descent method, the search algorithm, the Hungarian algorithm. Etc.)

image processing and pattern identification

高阶谱估计的函数方程,很有用处,用于检测被高斯信号淹没的信号(higher order spectral estimation function equation, useful for the detection of Gaussian signal was submerged signal)

This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. Before getting to a description of PCA, this tutorial first introduces mathematical concepts that will be used in PCA. It covers standard deviation, covariance, eigenvectors and eigenvalues. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.