CDMA-multiuser-detection-MATLAB-programs

2013 matlab basepaper

采用加权网络中节点强度和权重都是幂率分布的模型，双向PCS控制仿真，能量熵的计算，重要参数的提取，基于人工神经网络的常用数字信号调制，应用小区域方差对比，程序简单。( Using weighted model nodes in the network strength and weight are power law distribution, Two-way PCS control simulation, Energy entropy calculation, Extract important parameters, The commonly used digital signal modulation based on artificial neural network, Application of small area variance comparison, simple procedures.)

programme d optimisation et document pdf

LMS均衡算法 MATLAB语言 自己打开 里面有详细的解释和说明 ，希望大家下载，给大家帮助(LMS equalization algorithm based on MATLAB language yourself open there are detailed explanations and instructions, I hope you download, for everyone to help)

通过遗传算法对神经网络的结构进行优化,避免参数选取的盲目性(Through genetic algorithms to optimize the structure of neural networks, to avoid the blindness of parameter selection)

神经网络算法30例，里面是完整的，主要包含神经网络算法和聚类、灰色等。(30 cases of neural network algorithm, the inside is complete, mainly includes neural network algorithm and clustering, grey, etc.)

《精通matlab》（张志勇北航）。内容详尽，是进一步学习matlab较好的工具(" Proficient in matlab" (Zhang Zhiyong Beihang). Detailed, is a good tool for further learning matlab)

基于频率到谱技术的离散小波变换的音频识别技术(Frequency to the spectral techniques based on discrete wavelet transform audio recognition technology)

I am a third year master major in automation at Shanghai Jiao Tong University, P. R. China. With tremendous interest in Industrial Engineering, I am writing to apply for acceptance into your Ph.D. graduate program.

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.)