Onduleur-Simulink

关于MIMO天线的matlab仿真，有代码和仿真结果图(Matlab simulation on MIMO antenna with code and the simulation results)

TDOA/AOA定位的扩展卡尔曼滤波定位算法Matlab源码(TDOA/AOA positioning of the extended Kalman filter localization algorithm Matlab source code)

自己写的,在matlab环境下将rgb图像转为yuv文件,可适用于444,422,420的格式.(wrote it myself, in the Matlab environment will yuv rgb images to documents, applicable to the 444, 422,420 format.)

MATLAB平台下使用HOUGH变换检测圆的算法(Use HOUGH transform circle detection algorithm)

用离散傅里叶变换编写的人脸识别的代码，基于matlab平台，附带了专用的用于识别的人脸图片(Face recognition code using the discrete Fourier transform, based on matlab platform, comes with face photos dedicated used to identify)

matlab toolbox three level ,you can use it to learn matlab

基于3种储能策略对微电网的可靠性指标计算评估(Reliability evaluation of microgrid based on energy storage strategy)

说明：  指派问题 微粒群PSO算法优化 带绘图 优化目标为工作质量(Assignment of particle swarm optimization PSO algorithm to optimize the drawing target with the quality of work)

普通PID控制及其扩展算法控制的MATALB仿真程序，内容包含面广．(ordinary PID control algorithm and its expansion of enriching control simulation program, which includes many sectors.)

蕞优分组周游路线问题是一类物流智能调度问题，是众多的计算难度极大的组合爆炸问题之一。自从运输问题最早是由Hitchcock在1941年提出的[2][4]后，人们对这一类问题给予了极大的关注并进行了大量的研究，提出了有效解决问题的优化算法——单纯形法的变形[2]。但由于问题规模的扩大，纯数学方法在允许的时间和空间上都很难找到问题的最优解，于是人们开始转向寻找问题的近似最优解，遗传算法以其在求解最优化问题中的独特的自组织性、自适应性很快进入人们的视野。 经典的遗传算法对于求解多目标的NP完全性问题非常有效，但对于有多个限制条件的多目标最优化问题缺显得有点力不从心，很难得到稳定度较高、收敛较快的解。本文提出的双层遗传算法模型给这一类问题提供了一个很好的解决方法，虽然建模条件限制了描述的问题的通用性，有待推广，但它能有效利用问题的领域知识，提高了求解效率，保证了解的可行性和最优性，算法思想极具推广潜力，可在其它物流智能调度问题求解算法设计中借鉴。(The most optimal packet travel route problem is a kind of intelligent logistic scheduling problem is one of combinatorial calculation difficult problem many. Since the transportation problem was first proposed by Hitchcock in 1941 after the [2][4], the people of this class of problems given great attention and made a lot of research, the deformation of [2] optimization, simplex method algorithm is effective to solve the problem of. But due to the expansion of the scale of the problem, the pure mathematical methods in the allowed time and space are very difficult to find the optimal solution of the problem, so people begin to turn to the approximate optimal solution for the problem, genetic algorithm in solving optimization problems with its unique in the self-organizing, adaptive soon entered people s field of vision. Very effective classical genetic algorithm for solving multi-objective NP complete problems, but for the multi-objective optimization problem with multiple constraints. I)