invertergri

说明：  模糊数学聚类的matlab程序原代码，可用以对离散数据进行分类，综合评价。(Fuzzy clustering matlab program source code, can be used to classify the discrete data, comprehensive evaluation.)

说明：  Simple super resolution algorithm

特征提取，主要应用matlab对图像的特征进行提取，获得一些精度。(tezhengtiqu)

Lorenz系统的Poincare截面计算方法。用于分析转子点映射特性(Poincare cross-section of the Lorenz system calculation. Used to analyze the characteristics of rotor-point mapping)

该本书籍非常适合MATLAB初学者学习，书籍的内容涵盖的面很广，包括对软件的安装，界面的介绍，MATLAB语言，数值计算，程序设计，符号运算，simulink仿真的介绍。这本书的内容，既详细又有许多例子为辅来解释软件的各项功能。对于需要MATLAB作为科研工具的学者们，这是一本非常好的工具书。对于想学习MATLAB软件的同学们，也是一本非常好的学习教材。(This book is ideal for beginners to learn MATLAB, surface content covers a wide range of books, including the introduction of the installation of the software, interface, MATLAB language, numerical computation, program design, symbolic computation, simulink simulation presentation. Contents of this book, there are many examples of both, supplemented by detailed to explain the features of the software. The need for MATLAB as a research tool for scholars, this is a very good tool. For those who want to learn MATLAB software for students, but also a very good learning materials.)

MARKOv的应用 Step 1，求出各食品数据的增长率； Step 2，编程，使快速上升（>=0.03） ，在程序中用“2”表示；缓慢上升 (0,0.03)，在程序中用“1”表示；相对不变 0，在程序中用“0”表示；缓慢下降(-0.03,0) ，在程序中用“-1”表示；快速下降 (<=-0.03)，在程序中用“-2”表示；得到各类食品在不同时期的 ； Step 3，统计相邻增长率变化的一致的次数 ，就菜籽油而言，在2010-3-5到2011-3-15，其增长率为先快速增长后相对不变出现的次数为‘20’=1；具体42中食品的输出情况见附录； Step 4，对每一种食品，分五类进行叠加求和，分别为“先快速增长”、“先缓慢增长”、“先相对不变”、“先缓慢下降”、“先快速下降”；得到 ； Step 5，求出一步转移概率矩阵E ；即 每行的每五个数据依次与对应的 相除，得到概率矩阵E 的一行； Step 6，使用求出的一步转移概率矩阵E ，并求出不同时期的状态概率(MARKOv application Step 1, to obtain the growth rate of each food data Step 2, the programming, so fast rise (> = 0.03), in the program with " 2" slowly rising (0,0.03), used in the program " 1" indicates relatively constant 0, by the program " 0" indicates slow decline (-0.03,0), in the program by " -1" rapid decline (< =- 0.03), in the program with " -2" means get all kinds of food at different times Step 3, the same number of adjacent growth statistics change on rapeseed oil, in 2010-3-5 to 2011-3-15, Its growth rate is relatively rapid growth of the number of times after the first change occurs is ' 20' = 1 42 Output of specific food in Appendix Step 4, for each kind of food, five points are superimposed sum, respectively, " First of rapid growth," " first slow growth" , " first relatively unchanged" , " first slow down" , " first rapid decline" get Step 5, to obtain a step)

Generation of AWGN as a function of ExN0

It does walsh decoding in matlab.For w5 i.e. 100 Walsh code T = 01100110 For bit a3 at T/2 So a3=1 Similarly same things is do for T/4,T/8…….. Continuing the same steps we get w5=100

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)

upml barenger code in 2d