characteristic_induction_motor

基于Matlab的高斯差分金字塔图像产生程序，可直接输入一副图像，调用此函数就可以得到你所想要的所有金字塔图像。(Based on Matlab Difference of Gaussian pyramid image generation process can be directly input an image, calling this function you can get what you want all the pyramid image.)

设计一个模糊PID控制器,以及倒车系统simulink系统，使其可是实现对倒车系统的的优化控制。(Design a fuzzy PID controller, and the reversing system simulink system, so that it can achieve optimal control of the reversing system.)

介绍压缩采样新技术的优点，将其应用于阵列信号的处理中(Describes the advantages of sampling new compression technologies, the treatment applied to the array signals)

最小冗余最大相关性（MRMR）（MRMR.M） 需要外部库。详情请见MRMR。下载一个更新版本的互信息工具箱 偏最小二乘（PLS）回归系数（ReGCOEF.m） 使用MATLAB统计工具箱中的PLSReress ReliefF（分类）和RReliefF（回归）（ReleFracePr.M.） 从Matlab STATS工具箱中包装Releff.m。这是Matlab R2010B以后提供的。 ReliefF的另一个选择是使用ASU特征选择工具箱中的代码。这使用WEKA工具箱的ReleFEF，因此需要额外的库。请参阅相应的文档。 费雪评分（Fisher评分） 围绕ASFS特征选择工具箱围绕FSFisher。M(Minimum Redundancy Maximum Relevance (mRMR) (mRMR.m) Needs external library. See mRMR.m for details. Download a newer version of the mutual information toolbox Partial Least Squares (PLS) regression coefficients (regCoef.m) Uses plsregress.m from MATLAB statistics toolbox ReliefF (classification) and RReliefF (regression) (relieffWrapper.m) Wraps around relieff.m from the MATLAB stats toolbox. This is available MATLAB r2010b onwards. Another option for ReliefF is to use the code from ASU Feature Selection toolbox. This uses ReliefF from weka toolbox and hence needs additional libraries. Please see the corresponding documentation. Fisher Score (fisherScore.m) Wraps around fsFisher.m from the ASU Feature Selection toolbox)

一个很好的算法，大家可以看看！很有用！ (A good algorithm, we can see!)

this file contain matlab code for fir filter design

Matlab ebook in pdf format for easy understanding

用matlab实现混沌系统的李雅普诺夫指数图 分数阶程序。(using matlab to achieve Lyapunov and fractional order precedure)

求不规则波导的散射参数的matlab程序，使用的matlab的微分工具箱（有限元法），总的来说还不错，附带实验报告。(Scattering parameters of irregular waveguides matlab program, using a differential matlab toolbox (finite element method), on the whole pretty good, with a test report.)

卡尔曼滤波器的操作包括两个阶段:预测与更新。在预测阶段,滤波器使用上一状态的估计,做出对当前状态的估计。在更新阶段,滤波器利用对当前状态的观测值优化。(Kalman filter operation consists of two stages: prediction and update. In the prediction stage, the filter is estimated using the previous state, to make an estimate of the current state. In the update phase, the filter use observations on the current state of the optimization.)