tttttttttlpc.m

专业交易分析和心理分析书籍。适合所有喜欢交易的人阅读。(Professional transaction analysis and psychoanalysis books.)

本代码包含了利用可行点标记求最佳匹配算法的MATLAB代码，求最小费用最大流算法的PkyHyj程序，还有利用遗传算法解决 sYTJYqg 问题的Matlab程序。非常适合初学MATLAB的朋友学习。(The code contains the use of feasible point mark for the best match algorithm MATLAB code, find the minimum cost maximum flow algorithm PkyHyj program, as well as the use of genetic algorithms to solve the problem sYTJYqg Matlab program. Very suitable for beginners to learn MATLAB friends.)

这是Matlab实现的非常简单的数字信号调制仿真，用于Xilinx FPGA（ASK, BPSK, FSK, OOK, QPSK）(Matlab is very simple simulation of digital signal modulation for Xilinx FPGAs (ASK, BPSK, FSK, OOK, QPSK))

MATLAB三次样条插值法 求信号的包络线 源代码

generation of FSK in simulink

matlab入门教程加实际分析，数据矩阵 适合MATLAB初学者结合实例学习(matlab course with example for beginner)

matlab程序运行时导入数据文件作为输入参数，模式识别中的bayes判别分析算法，随机调制信号下的模拟ppm。( Import data files as input parameters matlab program is running, Pattern Recognition bayes discriminant analysis algorithm, Random ppm modulated analog signal unde.)

应用很广泛的卡尔曼滤波标准matlab程序，简单明了，适合初学者。(A wide range of standard Kalman filter matlab program, plain and simple, suitable for beginners.)

说明：  该程序包中包含传统拉普拉斯金字塔分解和重构的matlab代码，以及测试程序(The package includes the traditional Laplace pyramid decomposition and reconstruction of matlab code, and testing procedures)

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) )