optimal-feedback-controller

这是一个视频文件，它是关于遗传算法多目标优化的视频(This is a video file, it is about multi-objective genetic algorithm to optimize the video)

根据多行三列的实验数据（x,y,signal)作出contourf图。其中x,y为二位空间位置（或其他概念的二维），signal为在该位置的信号。此代码先将数据按位置不同求平均，再将signal按照其对应的x,y分配到二位矩阵内。最后作图。编这个代码的初衷是为了制作大脑hippocampus区域place cell 活动性的contour 图。(it s a code i made for plotting a contour graph of experimental data (x,y,signal). The data should be n*3 arrays which cannot be plot into contour directly. In fact, it can subplot 6*6 graphs or more, but speed might be slow..... especially, when you want more detailed graphs.)

基于MATLAB的移动通信信道建模与仿真(Mobile communication channel modeling and simulation based on MATLAB)

用于方程组求解的matlab实例源代码（赋使用说明）(Equations for the matlab source code)

Kirsch filter for image processing

完整的OFDM系统仿真程序，包括各种调制方式以及误码率计算(Complete OFDM system simulation program, including a variety of modulation and the bit error rate)

阵列信号的最基础的波束形成，也是最基本的理论算法(The most basic array signal beam-forming, is also the most basic theory algorithm )

PDstrip the open source strip theory computer code

说明：  算法实例，一个基于配电网的可靠性分析案例，运用蒙特卡洛法(Algorithm example, a reliability analysis case based on distribution network, using Monte Carlo method)

说明：  决策树(Decision Tree）是在已知各种情况发生概率的基础上，通过构成决策树来求取净现值的期望值大于等于零的概率，评价项目风险，判断其可行性的决策分析方法，是直观运用概率分析的一种图解法。由于这种决策分支画成图形很像一棵树的枝干，故称决策树。在机器学习中，决策树是一个预测模型，他代表的是对象属性与对象值之间的一种映射关系。Entropy = 系统的凌乱程度，使用算法ID3, C4.5和C5.0生成树算法使用熵。这一度量是基于信息学理论中熵的概念。 决策树是一种树形结构，其中每个内部节点表示一个属性上的测试，每个分支代表一个测试输出，每个叶节点代表一种类别。 分类树（决策树）是一种十分常用的分类方法。它是一种监督学习，所谓监督学习就是给定一堆样本，每个样本都有一组属性和一个类别，这些类别是事先确定的，那么通过学习得到一个分类器，这个分类器能够对新出现的对象给出正确的分类。这样的机器学习就被称之为监督学习。(Decision tree is a decision analysis method based on the known probability of occurrence of various situations, which can calculate the probability that the expected value of net present value is greater than or equal to zero, evaluate the project risk and judge its feasibility by constructing a decision tree. It is a graphic method of intuitively using probability analysis. Because this kind of decision branch is drawn as a graph, it is very similar to the branch of a tree, so it is called decision tree. In machine learning, decision tree is a prediction model, which represents a mapping relationship between object attributes and object values. Entropy = the disorder degree of the system, using algorithms ID3, C4.5 and C5.0, spanning tree algorithm using entropy. This measure is based on the concept of entropy in information theory.)