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三机组出力优化程序三机组出力优化程序三机组出力优化程序(san ji zu chuli you hua cheng xu)

Demo emf source code 1

RBF网络在matlab开发环境中的实现实例，有利于初学者学习与掌握(RBF network in matlab to achieve the development environment example, be helpful for beginners to learn and master )

卡尔曼算法在通信过程系统辨识中的应用的典型案例(Kalman algorithm in the system Identification )

dct image steganography

单极性归零波形的功率谱，占空比为0.5(Unipolar zeroing wave power spectrum, the ratio is 0.5)

轨道设计，确定着陆准备轨道近月点和远月点的位置，以及嫦娥三号相应速度的大小与方向。(Track design, determine the location of the landing point in recent months to prepare the track and Apolune, as well as the size and direction of the Chang-e III corresponding velocity.)

compensation of dead time effect of the inverter dead time effect causes the impact of the control performance of the INVERTER system, and pointed out that the dead time compensation, the key lies in the current phase of the acquisition, in order to overcome the practical zero of the current ambiguity of the system is proposed based on the two-phase stationary coordinate system under the feedforward dead-time compensation method. by the offset voltage of the three-phase output current cycle Fourier transform is found only compensation 1, 5, 7 harmonic components to eliminate the dead time effect. simulation and experimental results verify the correctness and feasibility of this approach.

MATLAB 数学工具软件 实例简明教程(MATLAB mathematical tools simple tutorial examples)

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.)