MacCormack

Smoothed bootstrap Newcomb s speed-of-light data are used in the book Bayesian Data Analysis b

A-D检验 本文是关于A-D检验的MATLAB程序，适用于序言A-D检测的同学，希望参考指正(AD test article is on the AD test MATLAB program, students apply to the preamble to AD detection, correct me want to refer to)

由图像序列恢复结构:输入至少3帧图像，既可以根据马义的算法求取图像所表针的环境的三维结构(Restore the structure by the image sequence)

simulation presure proses rig with NN backpropagation

说明：  非线性系统的C-C算法,程序运行良好,能为从事混沌方面研究的人员提供一定的便利.(Nonlinear Systems C-C algorithms, running good, to engage in research in the chaotic provide certain facilities.)

matlab源码，对稀疏编码算法的实现以及测试，内含详细程序。(The matlab source code, implementation and testing of sparse coding algorithm, containing detailed program.)

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 )

利用最小二乘法实现pi模型的离线辨识和在线辨识(Parameter identification of hysteresis PI model)

the matlab fem code for some beginers .you can have a look

Matlab Wireless Communications 应用例程(Routine application of Matlab Wireless Communications)