T型三电平逆变器

rls adaptive filter design using matlab software

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)... *()

Matlab编写的Hough算法，自己运行过的，可以直接使用...(This is a HOUGH Algorithm in Matlab. It can be run successfully.)

程序是基于元胞自动机的交通模型，主要是针对单车道、单车变数运动的描述(Program is based on cellular automaton traffic model for the description of the movement of bicycle paths, cycling variables)

程序尚不完善，固定提取了3个主成分，没有做寻求最佳主成分个数；没有做交叉有效性检验 (good)

直方图均衡算法，傅里叶变换，频域滤波 ，中值滤波算法实现(Common understanding of the gray image space enhance operations and realization of histogram equalization algorithm.)

kalman滤波的经典例程，对于学习和指导使用kalman滤波非常有帮助，还附有Kalman最经典的论文A New approach to Linear filtering and prediction problems以及非线性系统确定采样型滤波算法综述2012控制与决策(kalman filtering classic routines, learning and guidance for using kalman filter is very helpful, but also with Kalman most classic treatise A New approach to Linear filtering and prediction problems and nonlinear systems reviewed to determine the sample type filter algorithm 2012 Control and Decision)

说明：  在现代信号处理领域非常重要的方法。这是最新的改进算法！(in modern signal processing field is very important way. This is the latest improved algorithm!)

ANFIS神经网络在matlab开发环境中的实现实例，有利于初学者学习与掌握(ANFIS neural network in the matlab environment development to achieve the example, be helpful for beginners to learn and master )

Selection of weight function and plot of inverse of it.