SVMbyQuadprog

本论文对跟踪闭环系统进行了设计，主要应用的是Matlab中的仿真构件，可以很好的跟踪目标，而且在其中对闭环系统进行了详细介绍，涉及到了卡尔曼滤波(The thesis of the closed-loop system was designed to track the main application is the simulation of Matlab component, can be a good target tracking, but also on the closed-loop system in which a detailed introduction on to the Kalman filter)

通信信道的交织编码和解码matlab实现(Communication channel interleaving encoding and decoding)

对文档进行二进制处理的几种方法进行比较，分析和评估，得出一定的规律。(Several binary document comparison, analysis and assessment, and to come to a certain law.)

线性规划： CmpSimpleMthd(完整单纯形法) 整数规划(含0-1规划) DividePlane(割平面法) ZeroOneprog(枚举法) 二次规划 QuadLagR(拉格朗日法) ActivedeSet(起作用集法)(Linear Programming: CmpSimpleMthd (complete simplex method) Integer programming (including 0-1 Programming) DividePlane (Cutting Plane Method) ZeroOneprog (enumeration method) Quadratic Programming QuadLagR (Lagrangian) ActivedeSet (work set method))

Daubechies wavelet used to simulate the Daubechies wavelet

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 )

一种拓扑优化范围中的3维SIMP程序，可以多次修改(A 3-D SIMP program in the scope of topology optimization that can be modified many times)

simple object tracker code

DMCmatlab实现，方法有效，有详细说明，欢迎分享。(DMCmatlab realization, the method is effective, and have the detailed instructions, welcome to share. )

说明：  PSO-LSSVM分类测试程序代码模板,PSO优化LSSVM工具箱——分类部分(PSO-LSSVM Classification Test Program Code Template, PSO Optimizing LSSVM Toolbox-Classification Part)