整理好的宽带波束形成应用程序

说明：  单层感知器学习 单层感知器学习 单层感知器学习 单层感知器学习(AI)

gauss seidel program

一个将32路64kbps信息流合成一个基群的复用模块和相应的解复用模块，要能观察合成或分解后信息流的一致性，演示的最小时间刻度为时隙（一次采样，即1/8000秒）。-64kbps would be a 32-way flow of information into one group based reuse modules and the corresponding de-multiplexing module, to be able to observe the post-synthesis or decomposition of the consistency of the flow of information to demonstrate the minimum time scale for the time slot (one sample, that is, a/8000 seconds).

# Generative Adversarial Nets for Matlab only class 2 with GAN  class 0-9 with infoGAN  I use feature matching to train Generative model. (I define this Loss in the `/matlab/+dagnn/Feature_Match_Loss.m`) 1.Compile matconvnet by run `gpu_compile.m` which you should remove comment in it. 2.You can test this code by run `test_gan_3.m` or `test_gan_info.m`  3.If you wanna train this code, you can run `train_gan_3.m` or `train_gan_info.m`  You can find the network structure in `GDnet_3.m` and `GDnet_info.m`  # Some Details 1.I may miss some thing or not select a good initial parameter. So any advice is welcome. 

利用矩阵乘法解决的经典例题 或许对您的编程思维受益匪浅(Using matrix multiplication to solve the classic example of thinking may benefit your program)

实现边缘特征提取的功能有很强的特征提取功能能够边缘特征进行提取(Edge feature extraction to achieve the function of the characteristics of a strong edge feature extraction functions can be extracted)

英文版本的LBP算法描述，包括matlab实现的算法例程，对想学习LBP的人有用(English version of the LBP algorithm description, including the realization of the algorithm matlab routines for people who want to learn useful for LBP)

用DTW算法实现语音信号的特征对齐，解决了发音长短不一的模板匹配问题(Aligned with the DTW algorithm to achieve the characteristics of the speech signal, and solve the problem of template matching the pronunciation of varying lengths)

标准粒子群算法的实现思想基本按照粒子群算法(2) 标准的粒子群算法的讲述实现。主要分为3个函数。第一个函数为粒子群初始化函数 InitSwarm(SwarmSize......AdaptFunc)其主要作用是初始化粒子群的粒子，并设定粒子的速度、位置在一定的范围内。本函数所采用的数据结构如下所示： 表ParSwarm记录的是粒子的位置、速度与当前的适应度值，我们用W来表示位置，用V来代表速度，用F来代表当前的适应度值。在这里我们假设粒子个数为N，每个粒子的维数为D。(His thought of the implementation of the standard particle swarm algorithm according to the particle swarm algorithm (2)- the standard particle swarm algorithm about implementation. Mainly divided into three functions. The first function of particle swarm initialization function InitSwarm (SwarmSize... AdaptFunc), its main function is to initialize particle swarm particle, and set the particle s velocity, position in a certain range. The function of data structure is as follows: ParSwarm record table is the particle s position, speed and the current fitness value, we use W to represent the position, represented speed with a V, to represent the current fitness value with F. Here we assume that the number of particles N, each particle dimension for D.)

运用matlab的SimMechanics模块进行平面曲柄摇杆机构的仿真(The simulation using the SimMechanics module of MATLAB planar crank rocker mechanism)