DSPANDMATLAB

本程序用matlab仿真卫星及轨道，希望对大家的学习有所帮助(This procedure simulation using matlab and satellite orbit, and they hope to be helpful to study the U.S.)

用于文本分析和分类的pLSA(Probability Latent Semantic Analysis)的Matlab算法，含有测试数据及算法原理介绍。也可用于图像分析。相比原来的版本，增加了示例程序demo.m，以实现可视化效果，用户更容易使用和理解pLSA算法。(For text analysis and classification of pLSA (Probability Latent Semantic Analysis) of the Matlab algorithm with test data and algorithm theory description. Can also be used for image analysis. Compared to the original version, an increase of sample programs demo.m, to achieve the visual effect, the user can more easily use and understand the algorithm.)

信号子空间拟合，用matlab实现ssf算法，实现多维搜索，信号定位。(Signal subspace fitting algorithms to achieve ssf matlab, achieve multi-dimensional search, signal positioning.)

ieee 39 bus test system (New England Test System) in MATLAB/SimPowerSystem

径向神经网络matlab程序，希望对大家有所帮助(Radial basis function networ)

采用MATLAB进行图像的轮廓提取，此为最简单的提取程序。(Using MATLAB contour extraction of the image, this is the most simple extraction procedure.)

z bus formation code using modification

说明：  差分粒子群算法是一种新的模仿鸟类群体行为的智能优化算法，现已成为进化算法的一个新的重要分支。在基本粒子群算法和改进粒子群算法原理的基础上，实现了粒子群算法在函数优化、图像压缩和基因聚类中的应用。(Differential particle swarm optimization (DPSO) is a new intelligent optimization algorithm which imitates the behavior of bird swarm. It has become a new important branch of evolutionary algorithm. Based on the basic particle swarm optimization (PSO) algorithm and the improved principle of PSO, the application of PSO in function optimization, image compression and gene clustering is realized.)

粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhar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。 (Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation), and has Eberhar Dr. Dr. kennedy invention. Stems from the behavior of predatory birds, PSO with genetic algorithm is similar to an iterative optimization-based tools.)

本文主要是介绍GAMBIT建模的基元和过程(GAMBIT modeling are introduced and the process of primitive)