divide_sqrt_algorithm_simulation

matlab入门到提高，全面讲解了matlab的使用(Introduction to matlab improved overall on the use of the matlab)

说明：  实现flody算法，功能：计算任意两点之间距离(Achieve flody algorithm functions: calculate the distance between any two points)

raves controller with par

基于LMS的自适应波束成形,本程序给出了基于LMS算法的自适应波束成形(The process is the LMS algorithm based on adaptive beamforming based LMS adaptive beamforming)

channel estimation of combat pilot based technique, provides good estimation of ofdm channel

一个关于风力发电的仿真模型，应用的是双馈电机。(A simulation model of the wind power generation, application of the doubly-fed machine.)

支持向量机的实现代码,它在解决小样本、非线性及高维模式识别中表现出许多特有的优势。(Support vector machine code)

聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)

目标因子分析，用于找寻主因子数的个数，用途广泛(Target factor analysis, to find the main number for the number of factors, versatile)

用斜率的方法来求时间序列的lyapunov exponent,在非线性动力学和时间序列处理中经常用到。很实用的小程序。(slope of the methods used to the time series for the lyapunov exponent. Nonlinear Dynamics and the time sequence processing frequently used. Very small practical procedures.)