junhenghua

3088MHz波段超短波路径损耗的数值仿真研究(electromagnetic field Dynamic Link)

吕爱琴,田玉敏,朱明华论文中OFDM系统仿真系统仿真代码(Lvai Qin, Tian Yumin, Zhu Minghua paper OFDM system simulation system simulation code)

通信理论分析中，产生Nakagami-m 变量的随机数，并与理论PDF和CDF比较，以及第N个最大的Nakagami-m变量的仿真与理论PDFCDF的验证(he random numbers that produce the Nakagami-m variables are compared with the theoretical PDF and CDF, and the Nth Max and PDF/CDF of the largest Nakagami-m variables are verified by simulation and theory.)

matlab实例编程一百个源程序,包含了各个方面的实例编程，程序都经检验可以顺利运行，希望可以为初学matlab者提供帮助。(matlab examples of programming source 100, includes examples of all aspects of programming, procedures have been tested to run smoothly, I hope can provide to help novice matlab.)

Kernel smoothing regression

a sonar programing, radar

单相全桥逆变器模型，测试OK，直接运行，需要的参考(Single phase full bridge inverter model)

动态矩阵控制算法与PID算法结合,有很强的理论性,是毕业设计的好程序(Dynamic Matrix Control algorithm combined with the PID algorithm, is very theoretical, is a graduate design program)

小波变换文档汇总：二维小波变换在图像去噪处理中的应用、matlab小波工具箱及其应用、morlet小波变换理论与应用研究及软件实现、二维小波变换去噪的MATLAB可视化设计等(Wavelet transform document summary: two-dimensional wavelet transform in image denoising processing, matlab wavelet toolbox and its application, the application morlet wavelet transform theory and application research and software implementation, the 2 d wavelet transform denoising matlab visual design, etc )

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.)