Sequential_featureselection

跳频扩频通信系统仿真 (Frequency Hopping Spread Spectrum Communication System Simulation)

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.)

Steve Gunn 开发的svm工具箱源代码，用于分类和回归(Developed by Steve Gunn，used in classification and regression)

说明：  PD control for robotics

CC_LEVELS_CONSTRAINED: CC grids with constrained levels.

极值学习机，一种最近流行的算法。它的使用，使得计算过程大大的简化，值得交流分享。(Extreme Learning Machine, a recently popular algorithms. Its use, so that the calculation process is greatly simplified, worth sharing.)

Inside the voltage-input PV module PV Module Characteristics Simulink model

微电网多目标调度，运用改进的多目标粒子群算法进行计算，里面包含经济等三个目标函数设置((Smart Microgrid PSO, micro sources: photovoltaic, wind turbines, generators, energy storage, etc.))

基于MatLAB的图像的局部增强。采用梯度比较，将暗的区域变亮(local enhancement)

different types of signals matlab code