matlabfor循环矩阵求和,matlab用while循环创建矩阵

MATLAB矩阵运算与for循环

单层的for循环貌似与矩阵运算的区别不大
两层以上的for循环则明显慢于矩阵运算

遇到的问题

用MATLAB做蒙特卡洛仿真时，需要随机生成一个很大矩阵的随机数

for j = 1:length(Pj1) j Pj = Pj1(j);for k = 1:length(N1) k N = N1(k); tm(k) = erfenfa(@tm_mtcl,0,10,1e-3); tm1 = tm(k); if tm1<=9.9 for i = 1:M Jr=RR*sqrt(rand(1,N));Jseta=2*pi*rand(1,N);%协作结点 Jx=x0+Jr.*cos(Jseta);Jy=y0+Jr.*sin(Jseta); J = Jx + 1i * Jy; rtr = abs(R0)';rte = abs(E)';rje = abs(J-E)';rjr = abs(J-R0)'; hjr = exprnd(1,N,1).*(rjr.^(-alpha)); hje = exprnd(1,N,1).*(rje.^(-alpha)); hte = exprnd(1,1,1).*(rte.^(-alpha)); htr = exprnd(1,1,1).*(rtr.^(-alpha)); A = gt(tm1,hjr); %筛选 tm改 hje1 = hje.*A;rje1 = rje.*A;hjr1 = hjr.*A; ytr(i) = Pt*htr/(Pj*sum(hjr1)+ctr); end RcR = @(RR) -RR*(1-out_mt(ytr,RR)); [x,y] = fminbnd(RcR,0,5); Rcm1(k,j) = -y; Rm1(k,j) = x; else Rcm1(k,j) = 0; Rm1(k,j) = 0; endendend

采用矩阵运算之后

Jr=RR*sqrt(rand(M,N));Jseta=2*pi*rand(M,N);%协作结点Jx=x0+Jr.*cos(Jseta);Jy=y0+Jr.*sin(Jseta);J = Jx + 1i * Jy;rje = abs(J-E)';rjr = abs(J-R0)';hjr = exprnd(1,N,M).*(rjr.^(-alpha));hje = exprnd(1,N,M).*(rje.^(-alpha));hte = exprnd(1,1,M).*(rte^(-alpha));htr = exprnd(1,1,M);A = gt(tm,hjr);hje1 = hje.*A;hjr1 = hjr.*A;FA = Pj*sum(hje1,1)+cte;DM = Pt*hte+FA;ytr = Pt*htr./(Pj*sum(hjr1,1)+ctr);er1 = erP2(FA',DM',mu);mer = min(er1);f = mer-e;Rc = R.*(1-out_mt(ytr,R));Rcm = max(Rc);

运算速度能比原来提升10倍以上

bsxfun函数的使用

bsxfun（@fun，A，B）是功能很强大的一个函数，能够自定义三维矩阵与二维矩阵或一维矩阵的运算，配合permute函数（交换矩阵的维度）有很好的效果。

function f = Pfa_mt1(FA,mu)M = size(FA,1);for i = 1:length(mu)A = gt(FA,mu(i));P(i,:) = sum(A,1)/M;endf = P;end

修改后

function f = Pfa_mt2(FA,mu) a = size(FA,1);mu = permute(mu,[3,1,2]);c = bsxfun(@gt,FA,mu);P = sum(c,1)/a;f = permute(P,[3,2,1]);end

以上两种方式能实现同样的效果。