1111

PreProcessing of ECG signal : 50 Hz frq. filter, Base line wounder correction and ploting of graphs.

也是仿真的资料,是学仿真的好资料,你看看是应该不错的.(Also the simulation data is a good learning simulation, you look good.)

说明：  详尽的抑制载波双边带调制解调程序，有详细的注释说明(Detailed bilateral with carrier suppression modulation and demodulation process, detailed notes)

说明：  关于瑞利衰落的一段很实用的代码。特别在BPSK调制中对于学习通信方面的同学很有帮助。(Rayleigh fading on a very useful code. BPSK modulation, especially in communications for students to learn helpful.)

pll仿真,锁相环的设计与simulink下的仿真图。准确的。(pll simulation, phase-locked loop design with simulink under the simulation diagram. Accurate.)

自适应有限元求解第一类和第二类边界条件的拉普拉斯方程的相关子程序(Adaptive finite element solution of the first and second boundary conditions related to the Laplace equation subroutine)

matlab 小程序，初学者可以好好看看，学习知识。建议下载学习word版本(matlab examples knowledge)

IEEE30节点的电力系统仿真IEEE30节点的电力系统仿真(Simulation of power system simulation of IEEE30 node in IEEE30 node )

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.)

基于受控自回归积分滑动平均 (CARIMA)模型控对象的预测控制程序(Points based on the controlled auto-regressive moving average (CARIMA) Model Predictive Control control procedures)