HY4145

说明：  已经编好的关于函数逼近的常用MATLAB 算法(Approximation)

基于MATLAB的电子钢琴设计，可以实现基音按键发音和播放歌曲(signal generater and piano)

说明：  求误差函数的值（算法借鉴MATLAB中erf函数）(According the erf function in matlab program, get the value of ERROR function.)

描绘曲线，看曲线收敛性,稳定的系统是在时间的无限长时，曲线收敛，证明系统稳定，曲线发散，证明系统不稳定(we can look at zhe line and realize it is stable or not!)

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 )

系统辨识用matlab产生M序列，移位寄存器实现(System identification using matlab generate M sequence, the shift registers)

If you have not registered F567

基于comsol server的matlab代码，用于模拟二维等离子体辉光放电。(matlab code based on comsol server to simulate 2D glow discharge.)

DC to DC buck converter, with control loopDC to DC buck converter, with control loopDC to DC buck converter, with control loopDC to DC buck converter, with control loop

voicebox 语音识别中用到的一些常用函数，比较好用(voicebox asr)