beamforming

这本matlab简明教程介绍了很多数值算法，适合初学者(This book tell us some methods about mathatics)

This is the world matlab code for the classification of Pattern recognition, with the random and NN method

gauss消去法解线性方程组，系数矩阵A存放于数组A中(Gauss elimination method solving linear equations, A of the coefficient matrix A stored in an array)

This section of MATLAB source code covers 802.11a WLAN Channel Model.

apf仿真模型 谐波检测 抑制 低通滤波器(the apf simulation model of harmonic detection suppress low-pass filter)

FAN ECG Compression algorithm code

MATLAB最小生成树，将权数从小到大排序，按顺序添加，如果形成圈则去掉(MATLAB minimum spanning tree, the weights of small to large order, in order to add, if the ring is removed to form)

AT91SRAM7 configuration file is very good very practical hope to help you. Learn each other and work together. Take out a Shared

AdaBoost元算法属于boosting系统融合方法中最流行的一种，说白了就是一种串行训练并且最后加权累加的系统融合方法。 具体的流程是：每一个训练样例都赋予相同的权重，并且权重满足归一化，经过第一个分类器分类之后， 计算第一个分类器的权重alpha值，并且更新每一个训练样例的权重，然后再进行第二个分类器的训练，相同的方法....... 直到错误率为0或者达到指定的训练轮数，其中最后预测的标签计算是各系统*alpha的加权和，然后sign（预测值）。 可以看出，训练流程是串行的，并且训练样例的权重是一直在变化的，分错的样本的权重不断加大，正确的样本的权重不断减小。 AdaBoost元算法是boosting中流行的一种，还有其他的系统融合的方法，比如bagging方法以及随机森林。 对于非均衡样本的处理，一般可以通过欠抽样(undersampling)或者过抽样(oversampling)，欠抽样是削减样本的数目， 过抽样是重复的选取某些样本，最好的方法是两种进行结合的方法。 同时可以通过删除离决策边界比较远的样例。 (AdaBoost boosting systems dollar fusion algorithm is the most popular one, it plainly systems integration approach is a serial train and final weighted cumulative. Specific process is: Each training example is given equal weight, and the weights satisfy normalization, after the first classifiers after Calculating a first classifier weights alpha value for each sample and updates right weight training, and then the second classifier training, the same way ....... 0, or until the specified error rate training rounds, wherein the label is the calculation of the final prediction system* alpha weighted and then sign (predicted value). As can be seen, the training process is serial, and weight training examples is always changing, the right of the wrong sample weight continued to increase, the right to correct sample weight decreasing. AdaBoost algorithm is an element, as well as other methods of boosting popular systems integration, such as bagging and random forest method. For )

1.采用C均值聚类算法对男女生样本数据中的身高、体重2个特征进行聚类分析，考察不同的类别初始值以及类别数对聚类结果的影响，并以友好的方式图示化结果。 2.采用分级聚类算法对男女生样本数据进行聚类分析。分别尝试采用身高，体重，喜欢数学，喜欢文学，喜欢运动，喜欢模式识别共6个特征，以及身高，体重2个特征进行聚类，对比聚类结果，并以友好的方式图示化结果 (1. The use of C means clustering algorithm on sample data for boys and girls in the height, weight 2 characterized in cluster analysis to investigate the initial values of the different categories and the effect on the number of classes of the clustering result, and in a friendly way of illustration result. 2. Using hierarchical clustering algorithm to male and female students sample data clustering analysis. Try both using height, weight, like math, like literature, like sports, like pattern recognition feature a total of six, as well as height, weight, two features clustering, comparing clustering results, and in a friendly way of illustration of the results)