Adaboost

说明：  可以转动的风扇:rotx_.m,roty_.m,rotz_.m为旋转函数，在命令窗口调用fan_rot.m即可获得动态的旋转风扇的图形。(can turn on the fans : rotx_.m, roty_.m, rotz_.m for rotation function, called the order window fan_rot.m Get rotating fan dynamic graphics.)

The goal of this project is to analyze the properties of optical solitons.

T=[1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] 输入向量的最大值和最小值 threshold=[0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net=newff(threshold,[31 3],{ tansig , logsig }, trainlm ) 训练次数为1000，训练目标为0.01，学习速率为0.1 net.trainParam.epochs=1000 net.trainParam.goal=0.01 LP.lr=0.1 net = train(net,P,T) 测试数据，和训练数据不一致 P_test=[0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 (T = [1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] ' of the maximum and minimum input vector threshold = [0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net = newff (threshold, [31 3], {' tansig' , ' logsig' }, ' trainlm' ) training times for the 1000 target of 0.01 training, learning rate of 0.1 net.trainParam.epochs = 1000 net. trainParam.goal = 0.01 LP.lr = 0.1 net = train (net, P, T) test data, and training data inconsistencies P_test = [0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 )

基于云模型的PID控制算法，可以大大提高控制效率(PID control algorithm based on cloud model, can greatly improve control efficiency)

说明：  离散信源无失真信源编码（实现huffman编码） 可用于教学(Discrete source lossless source coding (to achieve huffman coding) can be used for teaching)

c++必备：c++ primer、clean code、effective c++(c++ primer、clean code、effective c++)

tool for measuring images

说明：  matlab 入门的相关的函数用法。。。。(matlab for the first time uses)

车用的轮速处理源代码，很好用，有滤波方法(EBD)

模糊逻辑工具箱函数包括定义语言变量及其隶属度函数、输入模糊推理规则、整个模糊推理系统的管理以及交互式地观察模糊推理的过程和输出结果。(Fuzzy logic toolbox functions including definition language variables and their membership functions, input fuzzy inference rules, the fuzzy inference system management and interactively observe fuzzy reasoning procedure and output result.)