derivatives

一共有15个m文件，80几个函数，5000多行代码。包含了一般软件的几大块：界面、参数、算法、仿真、演示等。熟悉GUI的朋友感兴趣的问题都有所涉及：比如参数传递，多个界面的操作，m文件构造复杂界面，存取文件，多个axes，读取图片，显示图片(A total of 15 m paper, 80 in several functions, more than 5000 lines of code. Contains several large pieces of software in general: the interface, parameters, algorithms, simulation, presentation and so on. Familiar with the GUI of the Friend of interest issues are involved: for example, transmission parameters, the operation of a number of interface, m file structure is complicated interface, access to documents, multiple axes, to read pictures, show the picture)

说明：  采用身高和体重数据作为特征，分别假设二者相关或不相关，在正态分布假设下估计概率密度，建立最小错误率Bayes分类器，写出得到的决策规则，将该分类器应用到训练/测试样本(Bayes classifier is the most classic! Minimize the classification error. With the matlab language, with examples)

it is a pdf file about H264 codec.

k近邻算法源代码，用matlab语言编写的，适用于分类及相关问题的分析(K neighbor algorithm source code, written with matlab language, suitable for classification and analysis of the related problem)

离散时间系统的时域分析 用多种方法实现目的(Time-domain analysis of discrete-time systems with a variety of methods to achieve the purpose)

极坐标形式的牛顿拉夫逊法 数值分析matlab源代码(Polar form of Newton Raphson Numerical analysis matlab source code)

压电智能悬臂梁振动主动控制的研究 压电智能悬臂梁振动主动控制的研究 (Piezoelectric active vibration control of smart cantilever beam study of piezoelectric active vibration control of smart cantilever beam research)

论文题目：基于Matlab无刷直流电机控制系统建模与仿真 为验证控制算法和控制策略的合理性，在 分析无刷直流电机数学模型的基础上，用Matlab建 立了无刷直流电机控制系统的仿真模型，并详细介 绍了控制系统的各个子模块。通过对实例电机的仿 真，给出了仿真结果，证明了该模型的可行性。(To verify the control algorithm and control strategy is reasonable, in the Analysis of brushless DC motor based on the mathematical model, using Matlab to build Li of the brushless DC motor control system simulation model, referred in detail Shao-control system, the various sub-modules. Through the examples of motor imitation True, gives the simulation results prove the feasibility of the model. )

the upsample and down sample function rebuild as new code resamplex

PID控制算法简单、鲁棒性强，但其参数整定过程繁琐，整定时需要控制对象的精确数学模型，而且整定往往是针对某一种具体工况进行的，缺乏自学习和自适应能力。模糊神经网络则兼备了模糊逻辑和神经网络的优点，具有函数逼近功能，具有较强的自适应、自学习能力、容错能力和泛化能力。借助于遗传算法对全局性参数进行优化设计，借助于BP算法对局部性参数进行优化，将模糊神经网络和遗传算法引入PID控制参数的整定过程，构造出一种基于模糊神经网络和遗传算法的智能PID控制器(Intelligent controller based on fuzzy neural network and genetic algorithm)