yibudiandongjifanjiezhidong

Moga- SVM procedures contain three documents : 1.moga 2.nsga

该程序演示了三角函数和双曲函数以及对数函数之间的关系，可供数学学习研究之用。 (The program demonstrates the trigonometric and hyperbolic functions and the relationship between the logarithmic functions are available for mathematics learning research.)

经典的功率谱估计方法，包含自相关法，周期图法(The classic power spectrum estimation methods, including autocorrelation, periodogram)

图像处理的MATLAB程序（教学使用）-非线性灰度变换法(MATLAB image processing procedures (Teaching)- linear gray level transformation method)

metodo para resolver polinomios, metodo de la biseccion

BURG方法谱估计 采用BURG迭代方法计算系数，在采用AR模型的谱估计进行谱峰搜索(BURG spectrum estimation methods BURG iterative method using coefficients in the AR model spectrum estimation using the peak search)

计算一维时间序列的Renyi熵，可作为脑电信号的特征提取方法，从而对脑电的复杂度进行分析(The Renyi entropy of one dimensional time series can be calculated as a feature extraction method of EEG signal, which can be used to analyze the complexity of EEG.)

介绍了一种将Matlab编译成可执行程序的一种方法，它使得Matlab编写的程序能够完全脱离Matlab这个环境来执行，从而大大提高了程序的应用范围和使用价值。(introduces a Matlab compiled executable in a way that allows programs written Matlab can be completely out of the Matlab environment to implementation, thus greatly improving the procedures for the application and usefulness.)

霍夫曼编码 是一种瞬时的、唯一可解码的分组编码。 MATLAB源文件，用于图像压缩 ，可添加到MATLAB路径，直接调用！(Huffman coding is a transient, only the encoding can be decoded packet. MATLAB source files, for image compression, can be added to the MATLAB path, directly call!)

试者工作特征曲线 （receiver operating characteristic curve，简称ROC曲线），又称为感受性曲线（sensitivity curve）。得此名的原因在于曲线上各点反映着相同的感受性，它们都是对同一信号刺激的反应，只不过是在几种不同的判定标准下所得的结果而已。接受者操作特性曲线就是以虚报概率为横轴，击中概率为纵轴所组成的坐标图，和被试在特定刺激条件下由于采用不同的判断标准得出的不同结果画出的曲线。(PLOTROC Plot receiver operating characteristic. Syntax plotroc(targets,outputs) plotroc(targets1,outputs1, name1 ,targets,outputs2, name2 , ...) Description PLOTROC(TARGETS,OUTPUTS) plots the receiver operating characteristic for each output class. The more each curve hugs the left and top edges of the plot, the better the classification. PLOTROC(TARGETS1,OUTPUTS2, name1 ,...) generates multiple plots. )