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matlab非常实用的程序 包括图像 函数 供初学者学习 很好用 (matlab very useful function of the program, including images used for beginners to learn good)

说明：  位置跟踪器的matlab仿真代码，使用滑模控制方法(Location tracker matlab simulation code, using the sliding mode control method)

《机器人数学导论》中的matlab程序，包含螺旋运动和李群计算的程序("robot Mathematics" Introduction to the Matlab procedures, including spiral movement and the procedures for calculating Li)

基于matlab的光伏电池半物理仿真系统的研究 本文从原理上得出简化的光伏电池板数学模型，并分析了在阴影条件下光伏电池板的运行规律，并加以改善，从而得到比较完善的光伏电池板模型。将光伏电池模型在 Matlab/Simulink 中建模并仿真，在 PC机上得出光伏电池 V-I,V-P 特性。最后将 PC 机上 Matlab/Simulink 建立的光伏电池模型通过 AD/DA 板卡于外界真实量交互，为在 Matlab/Simulink 上搭建光伏系统能量提供部分的半物理仿真提出了可行性和合理性。(Research on Photovoltaic Battery Semi-Physical Simulation System Based on Matlab In this paper, a simplified mathematical model of photovoltaic panels is derived in principle, and the operating rules of photovoltaic panels under shadow conditions are analyzed and improved to obtain a more complete photovoltaic panel model. The photovoltaic cell model was modeled and simulated in Matlab/Simulink, and the PV cell V-I, V-P characteristics were obtained on the PC. Finally, the photovoltaic cell model established by Matlab/Simulink on the PC is interacted with the actual amount through the AD/DA board, providing feasibility and rationality for the semi-physical simulation of building photovoltaic system energy on Matlab/Simulink.)

说明：  线性方程组的各种解法：求逆法、分解法、迭代法、特殊解法、非奇次线性方程组的解法(A variety of linear equations method: inverse method, decomposition method, iterative method, a special solution, non-singular at the solution of linear equations)

MATLAB程序，用小波变换的方法实现Haar分解与重构。(MATLAB program, the method used to achieve Haar wavelet transform decomposition and reconstruction.)

压缩感知(正交匹配追踪法Orthogonal Matching Pursuit)(Orthogonal Matching Pursuit)

地震 正演(Seismic forward procedure)

傅里叶变换与逆变换, Matlab程序。The main focus of this lab lab is converting between the frequency and time domain. (Fourier Transform Forward and inverse)

聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)