Data-type-conversion

利用matlab程序编码，实现人工智能训练，并给出实际例子(Matlab program code used to achieve artificial intelligence training, and gives practical examples)

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.)

％ A 2D homogeneous convection-diffusion case (u=exp(-ex*deta*x-ex*deta*y) with a square with ％ all Dirichlet boundary, note that reaction coefficient is not zero ％ by indirect BKM ( A 2D homogeneous convection-diffusion case (u = exp (-ex* deta* x-ex* deta* y) with a square with all Dirichlet boundary, note that reaction coefficient is not zero by indirect BKM)

gabor for fingerprint matlab

说明：  lms/rls的自适应均衡算法仿真源代码,两个函数打包(lms/rls adaptive equalization algorithm simulation source code, two functions are packaged)

说明：  此程序是对数据进行平滑的预处理方法，用来去除噪音的干扰(This program is smoothing the data in order to remove noise interference)

Implementation of an algorithm to recognize faces using PCA

FFT and IFFT 64 point radix-4

In this section i will provide you some basic examples for image processing using C# Example 1: Get the RGB and Luminosity histograms using the default C# classes

求图像的信噪比函数及峰值信噪比函数 很有用(Demand function is useful for image signal to noise ratio)