xiefen.rar_Fuzzy Histogram_模糊阈值分割_混沌 模糊_阈值

clear; clc; tic; % b=imread('E:\pic\图片\图片\baboon.png'); % b=imread('E:\实验结果\膨胀加噪实验结果\模糊核聚类ls-svm预测\残差\0014nsctjlsvm.bmp'); % b=imread('E:\pic\加噪\luoding.bmp'); b=imread('E:\图片\1-10.bmp'); % b=rgb2gray(b); a=b(:,:,1); % imhist(a); [mz,nz]=size(a); % 计算平均邻域灰度的一维灰度直方图 a0=double(a); a1=zeros(mz+2,nz+2); a1(2:mz+1,2:nz+1)=a0; a1(1,2:nz+1)=a1(2,2:nz+1); a1(mz+2,2:nz+1)=a1(mz+1,2:nz+1); a1(2:mz+1,nz+2)=a1(2:mz+1,nz+1); a1(2:mz+1,1)=a1(2:mz+1,2); a1([1 mz+2],[1 nz+2])=a1([2 mz+1],[2 nz+1]); a3=uint8(colfilt(a1,[3 3],'sliding',@mean)); a2=a3(2:mz+1,2:nz+1); %计算二维直方图 p=zeros(256,256); for i=1:mz for j=1:nz n1=a(i,j); n2=a2(i,j); p(n1+1,n2+1)=p(n1+1,n2+1)+1; end end pp=p./mz./nz; calp=zeros(511,1); h=zeros(511,1); for i=1:256 for j=1:256 if pp(i,j)~=0 calp(i+j-1)=calp(i+j-1)+pp(i,j); h(i+j-1)=h(i+j-1)+pp(i,j)*log(pp(i,j)); end end end aa=find(calp~=0); start=aa(1); last=aa(end); %确定基本粒子群算法的迭代次数和粒子个数 Iter = 50; Agents = 20; N = 4; %初始化基本粒子群优化算法的参数 vm=5; Wmax = 0.9; %最大惯性因子 Wmin = 0.1; %最小惯性因子 c1 = 2.0; %个体学习因子 c2 = 2.0; %群体学习因子 xmin = start; %粒子允许的最小位置 ymin = start; xmax = last-1; %粒子允许的最大位置 ymax = last-1; Vxmin = -vm; %粒子允许的最小速度 Vymin = -vm; Vxmax = vm; %粒子允许的最大速度 Vymax = vm; Xmin = [xmin,ymin]; Xmax = [xmax,ymax]; Vmin = [Vxmin,Vymin]; Vmax = [Vxmax,Vymax]; %R = 2*sqrt(Lmax^2+Hmax^2); R = 10*sqrt(2); %初始化第一个种群，Xpos为位置向量(起点坐标x，y)，Vpos为速度向量 %位置向量初始化,速度向量初始化 x=round(unifrnd(xmin,xmax,Agents,1)); vx=round(unifrnd(Vxmin,Vxmax,Agents,1)); % y=round(unifrnd(ymin,ymax,Agents,1)); for i=1:Agents y(i)=round(unifrnd(x(i)+1,ymax,1,1)); end vy=round(unifrnd(Vymin,Vymax,Agents,1)); Xpop=[x,y',vx,vy]; %生成小生境种群 pop = zeros(Agents/N,4,N); for i = 1:N pop(1:Agents/N,1:4,i) = Xpop((i-1)*Agents/N+1:i*Agents/N,1:4); end %初始化粒子适应度pFit、历史最佳适应度pBestFit和全局最佳适应度gBestFit pFit = zeros(N,Agents/N); pBestFit = pFit; gBestFit = zeros(N,1); ind = zeros(N,1); PregBestFit = 0; %初始化粒子历史最优位置pBest和全局最优位置gBest pBest = zeros(Agents/N,2,N); gBest = zeros(N,2); %初始化粒子停滞次数nc和适应度变化值VarFit % nc = zeros(N,Agents/N); % VarFit = % zeros(N,Agents/N);?????????????????????????????????????????????????????? % calp=calobjvalue(I); %迭代更新 for it = 1:Iter %计算适应度pFit并更新历史最优适应度pBestFit和全局最佳适应度gBestFit for j = 1:N for t = 1:Agents/N Theta = pop(t,1:2,j); %计算当前粒子的适应度pFit pFit(j,t) = ThetaFit3(Theta,calp,h); %更新个体历史最优适应度pBestFit if pFit(j,t) > pBestFit(j,t) pBestFit(j,t) = pFit(j,t); pBest(t,1:2,j) = pop(t,1:2,j); end end end %更新当前时刻子群全局最优位置gBest %和子群全局最佳适应度gBestFit for j = 1:N [gBestFit(j),ind(j)] = max(pBestFit(j,:)); gBest(j,1:2) = pBest(ind(j),1:2,j); end %更新前一时刻全局最佳适应度函数值PregBestFit和全局最优位置PregBest [PregBestFit,ind1] = max(gBestFit(:)); PregBest = gBest(ind1,1:2); %RCS小生境进化 s =0; for i = 1:N-1 for j = i+1:N D = sqrt((gBest(i,1)-gBest(j,1))^2+(gBest(i,2)-gBest(j,2))^2); if D < R if gBestFit(i) < gBestFit(j) gBestFit(i) = 0; else gBestFit(j) = 0; end end end end for i = 1:N if gBestFit(i) == 0 %对置零的最优个体重新初始化 gBest(i,1) = round(unifrnd(xmin,xmax)); gBest(i,2) = round(unifrnd(gBest(i,1)+1,ymax)); pop(ind(i),1:2,i) = gBest(i,1:2); Theta = pop(ind(i),1:2,i); pFit(i,ind(i)) = ThetaFit3(Theta,calp,h); pBestFit(i,ind(i)) = pFit(i,ind(i)); pBest(ind(i),1:2,i) = pop(ind(i),1:2,i); [gBestFit(i),ind(i)] = max(pBestFit(i,:)); gBest(i,1:2) = pBest(ind(i),1:2,i); s = 1; end end while(s ~= 0) s = 0; for i = 1:N-1 for j = i+1:N D = sqrt((gBest(i,1)-gBest(j,1))^2+(gBest(i,2)-gBest(j,2))^2); if D < R if gBestFit(i) < gBestFit(j) gBestFit(i) = 0; else gBestFit(j) = 0; end end end end for i = 1:N if gBestFit(i) == 0 %对置零的最优个体重新初始化 gBest(i,1) = round(unifrnd(xmin,xmax)); gBest(i,2) = round(unifrnd(gBest(i,1)+1,ymax)); pop(ind(i),1:2,i) = gBest(i,1:2); Theta = pop(ind(i),1:2,i); pFit(i,ind(i)) = ThetaFit3(Theta,calp,h); pBestFit(i,ind(i)) = pFit(i,ind(i)); pBest(ind(i),1:2,i) = pop(ind(i),1:2,i); [gBestFit(i),ind(i)] = max(pBestFit(i,:)); gBest(i,1:2) = pBest(ind(i),1:2,i); s = 1; end end end %对最劣粒子重新初始化 if mod(it,10) == 0 %达到一定代数 [G,Ind] = min(gBestFit(:)); for i = 1:Agents/N pop(i,1,Ind) = round(unifrnd(xmin,xmax)); pop(i,2,Ind) = round(unifrnd(pop(i,1,Ind)+1,ymax)); end for j = 1:Agents/N Theta = pop(j,1:2,Ind); pFit(Ind,j) = ThetaFit3(Theta,calp,h); if pFit(Ind,j) > pBestFit(Ind,j) pBestFit(Ind,j) = pFit(Ind,j); pBest(j,1:2,Ind) = pop(j,1:2,Ind); end end [gBestFit(Ind),ind(Ind)] = max(pBestFit(Ind,:)); gBest(Ind,1:2) = pBest(ind(Ind),1:2,Ind); end %变尺度混沌变异 b = 0; Pc = zeros(2,1); it0 = 0; m = 0; n = 2; gBest1 = zeros(1,2); for i = 1:N gBest1 = gBest(i,1:2); while(b==0||b==0.25||b==0.5||b==0.75) b = rand; end while(it0 < 10) b = 4*b*(1-b); for j = 1:2 Pc(j) = Xmin(j)+b*(Xmax(j)-Xmin(j)); m = 1-((it-1)/it)^n; gBest1(j) = round((1-m)*gBest1(j)+m*Pc(j)); end Theta = gBest1(1:2); Fit = ThetaFit3(Theta,calp,h); if Fit > gBestFit(i) gBest(i,1:2) = gBest1; break; end it0 = it0+1; end end %位置向量Xpos和速度向量Vpos更新 for i = 1:N for j = 1:Agents/N for t = 3:4 %惯性权重更新并产生随机数r1、r2 W = Wmax - it*(Wmax - Wmin)/Iter; r1 = rand; r2 = rand; %速度向量Vpos更新 pop(j,t,i) = W*pop(j,t,i) + c1*r1*(pBest(j,t-2,i) - pop(j,t-2,i)) + ... c2*r2*(gBest(i,t-2) - pop(j,t-2,i)); %限制更新后粒子速度在[Vmin,Vmax]范围内 if pop(j,t,i) > Vmax(t-2) pop(j,t,i) = Vmax(t-2); end if pop(j,t,i) < Vmin(t-2) pop(j,t,i) = Vmin(t-2); end %粒子位置更新 pop(j,t-2,i) = round(pop(j,t-2,i) + pop(j,t,i)); if pop(j,t-2,i) > Xmax(t-2) pop(j,t-2,i) = Xmax(t-2); end if pop(j,t-2,i) < Xmin(t-2) pop(j,t-2,i) = Xmin(t-2);