[PID]-Advanced-Control-Engineering-2001-Burns-Mat

Applied Numerical Methods Using Matlab ,Matlab在数值计算中的应用的经典书籍(Applied Numerical Methods Using Matlab is a classic book)

matlab例程，根轨迹，微分方程等 (Matlab routines, root-locus, differential equations, etc.)

此段程序应用MATLAB的动画模拟了地球卫星从太空返回地球的轨道路线。(This process applications MATLAB animation to simulate the Earth s return to Earth from space satellite track line.)

用MATLAB语言实现BCH的编码和译码(BCHcode and decode)

说明：  用matlab语言实现傅里叶切片定理，以及基于反投影及滤波反投影的CT图像重建的程序设计试验(Matlab language with Fourier slice theorem, and on the back projection and filtered back projection image reconstruction program design CT test)

Harris角点检测算法，程序亲自验证，可以使用(harris appendimages match)

用平面波展开法计算二维带缺陷态结构的光子晶体的能带图。matlab程序源代码(With a two-dimensional plane wave expansion method to calculate the band structure of photonic crystal defect states in the energy band diagram. matlab source code)

对二维图片进行中心放大，能控制放大的倍数，采用最近邻法(enlarge pictures from the center of it，can control the index for enlargement)

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 )

Sine pdf for users that work in the domain of signal processing. help how to improve the process and work