IIR

人工智能课的一些作业，对选人工智能这门课的有很大帮助(Artificial Intelligence course some of the work on artificial intelligence selection of great help for this class)

说明：  KNN分类练习，有100数据，进行简单的KNN练习(KNN classification exercise, 100 data, simple KNN exercise)

一种数字水印技术的研究与应用，采用MATLAB编程(A digital watermark technology and applications programming using MATLAB)

there are some clustering codes of matlab.

POWER quality improvement using statcom

matlab code for Optical Character Recognition (OCR)

OFDM系统中信道估计基于LS改进算法--基于DFT的信道估计算法实现(LS channel estimation in OFDM system based on the improved algorithm based on DFT channel estimation algorithm )

Relief算法中特征和类别的相关性是基于特征对近距离样本的区分能力。算法从训练集D中选择一个样本R，然后从和R同类的样本中寻找最近邻样本H，称为Near Hit，从和R不同类的样本中寻找最近样本M，称为Near Miss，根据以下规则更新每个特征的权重： 如果R和Near Hit在某个特征上的距离小于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻是有益的，则增加该特征的权重；反之，如果R和Near Hit在某个特征上的距离大于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻起负面作用，则降低该特征的权重。(The correlation between feature and category in Relief algorithm is based on distinguishing ability of feature to close sample. The algorithm selects a sample R from the training set D, and then searches for the nearest neighbor sample H from the samples of the same R, called Near Hit, and searches for the nearest sample M from the sample of the R dissimilar, called the Near Miss, and updates the weight of each feature according to the following rules: If the distance between R and Near Hit on a certain feature is less than the distance between R and Near Miss, it shows that the feature is beneficial to the nearest neighbor of the same kind and dissimilar, and increases the weight of the feature; conversely, if the distance between R and Near Hit is greater than the distance on R and Near Miss, the feature is the same. The negative effect of nearest neighbor between class and different kind reduces the weight of the feature.)

OFDM simulation by using Matlab

an article about a dvr simulation