FFT

Matlab simulation of 6DOF Fixed Mass Rigid body

基于kaiser窗的双谱线插值FFT谐波分析，插值与拟合的matlab实现，ofdm系统仿真 含16qam调制 fft 加窗 加cp等模块。( Dual-line interpolation FFT harmonic analysis kaiser windows, Interpolation and fitting matlab implementation, ofdm system simulation including 16qam modulation fft windowing modules plus cp.)

分数阶统一混沌系统同步程序可实现分数阶混沌系统同步，对研究分数阶系统非常有用(synchronizing fractional unied chaotic system realize synchronzation fractional unied chaotic)

clear all I=imread( lena.bmp ) figure imshow(I) I2=imrotate(I,-4, bilinear ) 逆时针旋转4度 figure imshow(I2) I3=fliplr(I) 垂直镜像 figure imshow(I3) I4=imresize(I,0.5, bilinear ) 缩小为原图的1/2 figure imshow(I4) A=double(I) 转换为double类型 计算7个不变矩 [nc,nr]=size(A) [x,y]=meshgrid(1:nr,1:nc) 得到网格 x=x(:) y=y(:) A=A(:) m.m00=sum(A) if m.m00==0 m.m00=eps end m.m10=sum(x.*A) m.m01=sum(y.*A) 计算均值 xmean=m.m10/m.m00 ymean=m.m01/m.m00 计算中心矩(li9_23.rar cm.cm00=m.m00 cm.cm02=(sum((y-ymean).^2.*A))/(m.m00^2) cm.cm03=(sum((y-ymean).^3.*A))/(m.m00^2.5) cm.cm11=(sum((x-xmean).*(y-ymean).*A))/(m.m00^2) cm.cm12=(sum((x-xmean).*(y-ymean).^2.*A))/(m.m00^2.5) cm.cm20=(sum((x-xmean).^2.*A))/(m.m00^2) cm.cm21=(sum((x-xmean).^2.*(y-ymean).*A))/(m.m00^2.5) cm.cm30=(sum((x-xmean).^3.*A))/(m.m00^2.5) im(1)=cm.cm20+cm.cm02 im(2)=(cm.cm20-cm.cm02)^2+4*cm.cm11^2 im(3)=(cm.cm30-3*cm.cm12)^2+(3*cm.cm21-cm.cm03)^2 im(4)=(cm.cm30+cm.cm12)^2+(cm.cm21+cm.cm03)^2 im(5)=(cm.cm30-3*cm.cm12)*(cm.cm30+cm.cm12)... *((cm.cm30+cm.cm12)^2-3*(cm.cm21+cm.cm03)^2)... +(3*cm.cm21-cm.cm03)*(cm.cm21+cm.cm03)... +(3*(cm.cm30+cm.cm12)^2-(cm.cm21+cm.cm03)^2) im(6)=(cm.cm20-cm.cm02)*((cm.cm30+cm.cm12)^2-(cm.cm21+cm.cm03)^2)... +4*cm.cm11*(cm.cm30+cm.cm12)*(cm.cm21+cm.cm03) im(7)=(3*cm.cm21-cm.cm03)*(cm.cm30+cm.cm12)... *((cm.cm30+cm.cm12)^2-3*(cm.cm21+cm.cm03)^2)... +(3*cm.cm12-cm.cm30)*(cm.cm21+cm.cm03)... *()

controller system for controlling the transfer function

原始数据存储为excell文件，先将数据进行预处理，再通过SVM进行模型的建立与预测。(The data are stored as a excell file, preprocessing the original data and builting the SVM model.)

Bias-corrected fuzzy c-means (BCFCM) clustering compensates for two sources of uncertainty by modeling noise and bias fields

本程序先生成一个“Blocks”信号，然后加上高斯白噪声，再用 db3 小波进行分解，(This procedure Mr. Cheng a " Blocks" signal, and then adding white Gaussian noise, and then ' db3' wavelet decomposition,)

可以用来进行股市预测的matlab代码，直接模仿就可以用了(Can be used for stock market prediction matlab code, you can use a direct imitation)

algebraic reconstruction technique using guide