glcm

Thus file is a pdf. Explains discretization algorith CAIM for MAtlab

说明：  matlab code for adding noise

此代码为图像处理技术中的沃尔什-哈达玛变换，实现图像的压缩处理(This code is an image processing technology Walsh- Hadamard transform, image compression)

很好的传递对准程序 ，讲述的是有关于 挠曲变形建模型的(Transfer alignment procedure is very good, is told about the flexural deformation model)

说明：  DE Algorithm engine for LP and ILP

color histogram of an image

机器学习中的垃圾邮件分类程序，用matlab做的。从以下链接下载垃圾邮件数据（spam data）：(数据已下载，放在spambase.zip) http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html 该数据包含57个邮件信息相关的变量，每条邮件可以被分类为垃圾邮件（Y=1）和非垃圾邮件（Y=0）。输出Y的值在文件中每一列的末尾。练习的目标是要预测电子邮件是否为垃圾邮件。 (Machine Learning spam classification procedures, using matlab to do. Data (spam data) from the following link to download the junk mail: (data has been downloaded, put spambase.zip) http://www-stat.stanford.edu/ ~ tibs/ElemStatLearn/index.html The data includes 57 e-mail messages related variables, each message can be classified as spam (Y = 1) and non-spam (Y = 0). Y value of the output end of each column in the file. The goal is to predict exercise email is spam.)

CSTR模型cstr模型的建立，CSTR模型cstr模型的建立(CSTR model)

对MFO算法飞蛾位置更新方法进行改进，加快其收敛速度(The moths position updating method of MFO algorithm is improved to accelerate its convergence speed)

梯度下降法是最早最简单，也是最为常用的最优化方法。梯度下降法实现简单，当目标函数是凸函数时，梯度下降法的解是全局解。一般情况下，其解不保证是全局最优解，梯度下降法的速度也未必是最快的。梯度下降法的优化思想是用当前位置负梯度方向作为搜索方向，因为该方向为当前位置的最快下降方向，所以也被称为是”最速下降法“。最速下降法越接近目标值，步长越小，前进越慢。(The gradient descent method is the earliest and most simple and most commonly used optimization method. The gradient descent method is simple to realize. When the objective function is a convex function, the solution of the gradient descent method is a global solution. In general, the solution is not guaranteed to be the global optimal solution, and the gradient descent method is not necessarily the fastest. The optimization idea of gradient descent method is to use the current position negative gradient direction as the search direction, because the direction is the fastest descending direction of the current position, so it is also called the steepest descent method. The faster the slowest descent approach is closer to the target, the smaller the step, the slower the progress.)