High_dimensional_function_optimization

Download Now: Title: 802.11b PHY MATLAB Code Author: Stuart McGarrity Summary: 802.11b PHY MATLAB Code. MATLAB Release: R13 Required Products: Communications Toolbox,Signal Processing Toolbox Description: BER simulation of DBPSK, 1Mbps mode of 802.11b. Assumes perfect synchronization. Should work in earlier releases. (Download Now: Title: 802.11b PHY MATLAB Code Author: Stuart McGarrity Summary: 802.11b PHY MATLAB Code. MATLAB Release: R13 Required Products: Communications Toolbox, Signal Processing Toolbox Description: BER simulation of DBPSK, 1Mbps mode of 802.11b. Assumes perfect synchronization. Should work in earlier releases.)

MATLAB 教程 含源程序(MATLAB Tutorial with source code)

3D image visualization

将两个矩阵的卷积运算转换为构造的新矩阵的相乘运算(The two matrix of convolution operation into the matrix multiplication)

The attached file provides information to those interested in learning matlab.

自己写的程序，处理人体三维数据手部数据时，对指定域进行简化(Write their own procedures when dealing with three-dimensional data of human hand data on the specified domain to simplify)

包含CV、CA、Single、当前、恒转弯速率、转弯模型，小波包分析提取振动信号中的特征频率，包括 MUSIC算法，ESPRIT算法 ROOT-MUSIC算法。( It contains CV, CA, Single, current, constant turn rate, turning model, Wavelet packet analysis to extract vibration signal characteristic frequency, Including the MUSIC algorithm, ESPRIT algorithm ROOT-MUSIC algorithm.)

fs1016w编码标准matlab源码，亲自调试(matlab source coding standard fs1016w personally debugging)

Fuzzy logic simulink- vehicle control

说明：  采用栅格对机器人的工作空间进行划分，再利用优化算法对机器人路径优化，是采用智能算法求最优路径的一个经典问题。目前，采用蚁群算法在栅格地图上进行路径优化取得比较好的效果，而利用遗传算法在栅格地图上进行路径优化在算法显得更加难以实现。 利用遗传算法处理栅格地图的机器人路径规划的难点主要包括：1保证路径不间断，2保证路径不穿过障碍。 用遗传算法解决优化问题时的步骤是固定的，就是种群初始化，选择，交叉，变异，适应度计算这样，那么下面我就说一下遗传算法求栅格地图中机器人路径规划在每个步骤的问题、难点以及解决办法。(It is a classical problem to divide the workspace of the robot by grids and optimize the path of the robot by using optimization algorithm. At present, the ant colony algorithm is used to optimize the path on the grid map, and the genetic algorithm is used to optimize the path on the grid map, which is more difficult to achieve. The difficulties of using genetic algorithm to deal with the path planning of robot on raster map mainly include: 1. guaranteeing that the path is uninterrupted, 2. guaranteeing that the path does not cross obstacles. The steps of genetic algorithm in solving optimization problems are fixed, that is, population initialization, selection, crossover, mutation, fitness calculation. Then I will talk about the problems, difficulties and solutions of genetic algorithm in each step of robot path planning in raster map.)