PSOMATLAB

说明：  XO Game with GUI 是一款用matlab编写的待用户界面的人工智能游戏。 This is an improvement on the previous text-based game that was previously created. This file features a GUI with the same AI and sound effects. The Computer plays based on very simple rules, yet it is difficult to beat. (XO Game with GUI is a matlab prepared to be used user interface artificial intelligence game. This is an improvement on the previous text-based game that was previously created. This file features a GUI with the same AI and sound effects. The Computer plays based on very simple rules, yet it is difficult to beat.)

运用分水岭的算法对图像进行分割 是一种新的方法，运用此方法，还可以对重叠的细胞进行分割(WATERSHED SEGMENTATION)

用MATLAB语言描述的关于QPSK的无线通信系统仿真应用，如何产生QPSK信号(MATLAB language to describe the use of QPSK wireless communication system simulation applications, how to generate QPSK signal)

说明：  本文是关于在声波信号处理领域极为流行的S变换数学理论，英文版，仅供参考(This article is about the most popular acoustic signal processing, mathematical theory of S-transform, in English, for reference only)

图论中最短路径算法Dijkstra算法实现，很有用的(Shortest path algorithm Dijkstra algorithm in graph theory, useful)

非线性几何的matlab程序，使用matlab进行了仿真。程序代码简洁，有详细注释(Nonlinear geometry of the matlab program, the use of MATLAB simulation. The code is simple, there are detailed notes )

读入图像到命令窗口然后按CTRL + ENTER键，执行每一个单元。增强或降低图像亮度，不是普通的灰度调制。(first read any two image in command window then press ctrl+enter to execute each cell .highlight duller value nd increase or decrease the value from cell box to increase or decrease the image)

基于DMRS参考信号同步定时算法，信道环境为高斯信道，(DMRS based synchronization timing reference signal algorithm, the channel environment for Gaussian channel,)

改程序为切比雪夫拓扑结构的直线阵的matlab代码(Reform program for the Chebyshev linear array topology matlab code)

粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhart博士和kennedy博士发明。源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域(Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation), has Dr. Eberhart and Dr. kennedy invention. Deriving from the behavior of birds of prey PSO with genetic algorithm is similar to an iterative optimization-based tools. System initialization for a group of random solutions, through the iterative search for optimal values. But there is no cross-genetic algorithm used (crossover) and mutation (mutation). But the particles in the solution space of the particles to follow the optimal search. In detail the steps after the introduction sections compared with the genetic algorithm, PSO has the advantage of being simple and easy and did not realize many of the parameters need to be adjusted. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications)