GAfy

This file is matlab codes for Differentional Evoloution

这是关于matlab语言学习的经验，其中包括两个txt文档很方便下载，可以在手机上看，写的很好，值得看看。(This is about the matlab language learning experience, including two txt documents easy to download, you can phone point of view, well written, it is worth a look.)

说明：  此程序很好的解决了六自由度平台的仿真，我经过试验，很有效果！(this procedure very well resolve the 6-DOF simulation platform, I tried very effective!)

地震褶积模型的设计，采用的是雷克子波。速度模型设计引入了弱反射层(Seismic convolution model of the design, using the wave Leike Zi. Velocity model designed to introduce a weak reflection layer)

粗糙集理论的属性约简方法及应用研究，很有用的哈！！！！(Rough set attribute reduction method and application, very useful, ha! ! ! !)

本程序描述了无导师学习的神经网络用于一般的分类问题(This procedure describe no mentor learning neural network used for general classification problem)

说明：  压缩感知（Compressive Sensing，CS），有时也叫成Compressive Sampling。相对于传统的奈奎斯特采样定理——要求采样频率必须是信号最高频率的两倍或两倍以上（这就要求信号是带限信号，通常在采样前使用低通滤波器使信号带限），压缩感知则利用数据的冗余特性，只采集少量的样本还原原始数据。(（Compressive Sensing，CS）)

ROC curves illustrate performance on a binary classification problem where classification is based on simply thresholding a set of scores at varying levels. Lenient thresholds give high sensitivity but low specificity, strict thresholds give high specificity but low sensitivity the ROC curve plots this trade-off over a range of thresholds (usually with sens vs 1-spec, but I prefer sens vs spec this code gives you the option). It is theoretically possible to operate anywhere on the convex hull of an ROC curve, so this is plotted too. The area under the curve (AUC) for a ROC plot is a measure of overall accuracy, and the area under the ROCCH is a kind of upper bound on what might be achievable with a weighted combination of differently thresholded results from the given classifier

基于模型的风力发电机资料，有需要的学习一下吧(Model-based wind turbine data, there is a need to learn about it)

最近做的作业，费了好大劲才搞出来的MUSIC算法代码，需要的人可以借鉴额(Do the job, took great perspicacity to engage in the MUSIC algorithm code, people in need can learn from the amount of)