kpc_mm

说明：  匈牙利法与最佳匹配分配问题 matlab实现(Hungary and France and to achieve the best matching distribution matlab)

北斗二代B1频点接收机捕获Matlab源代码(BD2B1 Matalab Receiver)

用matlab中的工具箱函数来解决遗传算法问题，以及进行若干实例讲解 (using Matlab toolbox functions of the genetic algorithm to solve the problem, as well as a number of examples to explain)

This model can produce the characteristic of induction motor

此程序为信号阶次分析，主要针对汽车上振动噪声信号(Order this program for signal analysis, the main noise and vibration signals for the car)

发送装置：随机信号产生、脉冲成型（上采样可调，滚降可调）； 发送：AWGN信道（信噪比范围可调） 接收设备：QPSK调制解调 ，匹配滤波，判决 画出接收信号的眼图、星座图；计算误码率并与理论值比较。 (Transmitting device: random signal generator, pulse shaping (the sampling adjustable, adjustable roll-off) Send: AWGN channel (SNR range is adjustable) Receiving equipment: QPSK modulation and demodulation, matched filtering, judgment   BER is calculated and compared with the theoretical value draw the received signal eye diagram, constellation.)

预测控制算法matlab仿真，采用的是二阶系统，本程序是动态矩阵控制的简单仿真(Predictive control algorithm matlab simulation, using the second-order systems, this procedure is a simple dynamic matrix control simulation)

PID算法的具体事例在MATLAB环境下的源代码，希望对大家有帮助(Specific examples of PID algorithm in the MATLAB environment, source code, we want to help)

panorama image code

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.)