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说明：  四种常见的归一化方法，归一化效果不错。应用在matlab环境下。(Four common normalization method, normalized results are good. Used in the matlab environment.)

这是一个关于分数阶的程序，主要用于分数阶传递函数的matlab显示(Fractional order program)

用Mie理论计算双层粒子的散射角——散射光强曲线 1．RB1.m 计算第一类Ricatti-Bessel函数的函数 2．RB2.m 计算第二类Ricatti-Bessel函数的函数 3. Alegendr.m 计算角函数 和 的函数 4. MieCoeff.m 计算包覆粒子米尔系数的函数 5. Amp.m 计算散射光复振幅的函数 6. MieIntensity.m 画包覆粒子散射角-光强图 (Mie theory calculations using double-particle scattering angle- scattering intensity curve 1. RB1.m calculate the first class Ricatti-Bessel function of the function 2. RB2.m calculate the second category Ricatti-Bessel function of the function 3. Alegendr.m computing angle functions and function 4. MieCoeff.m coated particles Mill coefficient calculation function 5. Amp.m calculate the scattering amplitude recovery function 6. MieIntensity.m painting coated particle scattering angle- light intensity diagram)

在matlab软件中，自己编程实现的一个分类算法，算法首先采用kmeans与fcm聚类分析方法进行采样，然后利用svm对选取得样本进行分类(In matlab software, own programming to achieve a classification algorithm, the algorithm first used fcm kmeans cluster analysis methods and sampling, and then use the elections to obtain samples for svm classification)

EEMD在EMD的基础上进行添加高斯白噪声，VMD相对于EEMD时频分解效果更好，有效的抑制了模态混叠和端点效应(EEMD adds Gaussian white noise on the basis of EMD, and VMD has better effect on time-frequency decomposition than EEMD, effectively suppressing modal aliasing and endpoint effects)

SOLVE PROGRAM RBF Interpolation BY ICA

用matlab编写的递推的极大似然辨识程序(err)

自己编写的matlab gui可以实现贝塞尔曲线的参数选择和拟合效果(I have written matlab gui Bezier curves can realize the parameters of selection and fitting the effect of)

這是一個 Matlab的（和獨立應用）端口的出色的機器學習算法，隨機森林` - 由Leo布賴曼等。從 R -源由Andy鴻等。 http://cran.r-project.org/web/packages/randomForest/index.html（Fortran的原由Leo布賴曼和Adele卡特勒，研究港口安迪鴻和馬修維納。）當前的代碼版本是基於 4.5-29從來源 randomForest包。(This is a Matlab (and Standalone application) port for the excellent machine learning algorithm `Random Forests - By Leo Breiman et al. from the R-source by Andy Liaw et al. http://cran.r-project.org/web/packages/randomForest/index.html ( Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener.) Current code version is based on 4.5-29 from source of randomForest package.)