MATPOWER

Artifact removal of EEG signal EEG signal

粒子群算法的基本程序，文中举的是最简单的例子(Particle swarm optimization of the basic procedures, the paper cited is the simplest example of)

说明：  Genetic Algorithm for two variable

power_steppermotor.rar,功率电子领域matlab仿真文件，已经验证过，程序运行正常(power_steppermotor.rar,Power electronics field matlab simulation file, has already been verified, the normal operating procedures)

Apriori算法 matlab 自己在网上找的比较全的代码(Apriori algorithm matlab yourself online to find a comparison of the code)

说明：  简单的CI融合MATLAB程序，生成实际误差的协方差椭圆用于验证融合精度。(A simple CI fusion matlab program generates a covariance ellipse of the actual error to verify the fusion accuracy.)

在理想的信道状态条件下，对MIMO-OFDM系统进行仿真，包括信道估计，调制，解调，信道编解码等(In an ideal channel state conditions, the MIMO-OFDM system is simulated, including channel estimation, modulation, demodulation, channel coding and decoding)

fiber optic gyroscope Allan variance and kalman filter matlab program

对双线性时滞系统进行T-S模糊控制，满足系能指标，鲁棒控制(Bilinear time-delay system for TS fuzzy control system able to meet the targets, robust control)

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.)