Matlab 仿真

QAM实现程序 此程序实现了QAM信号的输出，通俗易懂。(QAM procedures to achieve the realization of this process the output of the QAM signal, easy to understand.)

说明：  在Matlab中输入任意矩阵，就可进行LU分解。(Matrix by LU decomposition)

maze solution. it is a solution for maze problem with matlab code. in this solution I used very simple functions and it is easy for everyone.

近红外光谱小波变换预处理方法，能将信号分解成一系列小波函数的叠加，这些小波函数都是由一个亩小波函数平移和伸缩得到的。(Near infrared spectra pretreatment method, wavelet transform signal can be decomposed into a series of wavelet function, the wavelet function is a function by a mu wavelet translation and scaling. )

本程序实现的是径向基神经网络的非线性函数函数逼近功能。(The program implementation is a nonlinear function of radial basis function neural network function approximation.)

该程序可以实现对于信号的时域参数提取以及信号的包络分析(The program can realize time domain parameter extraction of signals and envelope analysis of signals)

二自由度车模型结合驾驶员模型，从其他地方下下来的，还没看太明白，在此分享，希望一起学习一起进步

利用分支定届算法实现线性混合整数规划模型的求解（MATLAB代码）( using branch algorithm to solve the mixed integer linear programming model (MATLAB code))

Heed protocal for ad hoc netork

采样频率 fs=10000 轴承外环故障信号 fid=fopen( bearingout.dat , r ) 故障 N=1024 xdata=fread(fid,N, int16 ) fclose(fid) xdata=(xdata-mean(xdata))/std(xdata,1) 时域波形 figure(1) plot(1:N,xdata) xlabel( 时间 t/n ) ylabel( 电压 V/v ) db10小波进行4层分解 一维小波分解 [c,l] = wavedec(xdata,4, db10 ) 重构第1～4层细节信号 d4 = wrcoef( d ,c,l, db10 ,4) d3 = wrcoef( d ,c,l, db10 ,3) d2 = wrcoef( d ,c,l, db10 ,2) d1 = wrcoef( d ,c,l, db10 ,1) ( Sampling frequency fs = 10000 bearing outer ring fault signal fid = fopen (' bearingout.dat' , ' r' ) failure N = 1024 xdata = fread (fid, N, ' int16' ) fclose (fid ) xdata = (xdata-mean (xdata))/std (xdata, 1) time-domain waveform figure (1) plot (1: N, xdata) xlabel (' Time t/n' ) ylabel ( ' voltage V/v' ) db10 wavelet decomposition 4 layer one-dimensional wavelet decomposition [c, l] = wavedec (xdata, 4, ' db10' ) 1 ~ 4 reconstructed detail signal d4 = wrcoef (' d' , c, l, ' db10' , 4) d3 = wrcoef (' d' , c, l, ' db10' , 3) d2 = wrcoef (' d' , c, l, ' db10' , 2) d1 = wrcoef (' d' , c, l, ' db10' , 1) )