matlab_lesson_using_source_code

this the atp_emtp model of the tcsc with the sampling of the signal this is one of the advanced sofware call atp-emtp used to get the most accurate transients

Merge two sorted vectors a and b into a sorted vector

仿真实现了目标跟踪的粒子滤波跟踪，并对误差进行了统计( realize the particle filter)

奇异点是各种瞬态信号的主要特征, 奇异点的类型多种多样。(The singular points of various transient signal characteristics of a variety of types of singular points.)

微波与天线，课程中基于MATLAB的虚拟实验。对上课很有帮助(Microwave and antenna, the virtual experiment based on MATLAB course. Very helpful for class)

利用matlab软件对微电网光伏并网过程进行仿真，针对微电网的并网模式进行研究，实现微电网并网和孤岛两种状态的过渡(Matlab software to simulate the micro-grid photovoltaic grid for the microgrid grid mode research, micro power grid and island state transition)

基于拉格朗日多项式差值，已知一些给定的数值点（xi，yi）求未知的数xt对应的yt的值。(Lagrange polynomial based on the difference between the known value of some given point (xi, yi) find the value of an unknown number of xt corresponding yt.)

空间谱估计music 谱估计方法,估计信号参数(Spatial spectrum estimation music spectrum estimation methods, estimates signal parameters)

This is a complete simulation for illustrating MSK & GMSK modulation techniques spectral Efficiency.

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 )