Matlab-axis-settings

MATKABB NICE VERY VERY

SERVO_CONTROL for motor

归一化小波矩，非常简单易懂，程序完美，找的非常辛苦，人品担保，绝对可用！！！(Normalized wavelet moment, very easy to understand, the program perfectly, looking very hard, character guarantees, absolutely free! ! !)

some of gui and file matlab for student

基于全程快速微分器的PID控制matlab m文件仿真模型\作图(Based the full fast differentiator PID control matlab m file simulation models mapping)

explicit FDM for 1_2D_parabolic partial differential equation

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.)

two methods of calculating eucledian distance are shown in the m files

spectral element method

车牌识别定位程序的部分功能，通过虚拟阵元进行DOA估计，最大似然（ML）准则和最大后验概率（MAP）准则，是一种双隐层反向传播神经网络，通过反复训练模板能有较高的识别率，多目标跟踪的粒子滤波器，最小二乘回归分析算法。( Part of the license plate recognition locator feature, Conducted through virtual array DOA estimation, Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criterion, Is a two hidden layer back propagation neural network, Through repeated training IJIbFEAlate have higher recognition rate, Multi-target tracking particle filter, Least-squares regression analysis algorithm.)