粒子群算法优化RBF网络,用PSO优化径向基神经网络的权值，简单好用.zip

clear all close all %G为迭代次数,n为个体长度(包括12个参数),m为总群规模 %w,c1,c2为粒子群算法中的参数 G =250; n = 12; m = 20; w = 0.1; c1 = 2; c2 = 2; for i = 1:3 MinX(i) = 0.1*ones(1); MaxX(i) = 3*ones(1); end for i = 4:1:9 MinX(i) = -3*ones(1); MaxX(i) = 3*ones(1); end for i = 10:1:12 MinX(i) = -ones(1); MaxX(i) = ones(1); end pop = rands(m,n); for i = 1:m for j = 1:3 if pop(i,j) < MinX(j) pop(i,j) = MinX(j); end if pop(i,j) > MaxX(j) pop(i,j) = MaxX(j); end end for j = 4:9 if pop(i,j) < MinX(j) pop(i,j) = MinX(j); end if pop(i,j) > MaxX(j) pop(i,j) = MaxX(j); end end for j = 10:12 if pop(i,j) < MinX(j) pop(i,j) = MinX(j); end if pop(i,j) > MaxX(j) pop(i,j) = MaxX(j); end end end V = 0.1*rands(m,n); BsJ = 0; %根据初始化的种群计算个体好坏,找出群体最优和个体最优 for s = 1:m indivi = pop(s,:); [indivi,BsJ] = chap10_3b(indivi,BsJ); Error(s) = BsJ; end [OderEr,IndexEr] = sort(Error); Error; Errorleast = OderEr(1); for i = 1:m if Errorleast == Error(i) gbest = pop(i,:); break; end end ibest = pop; for kg = 1:G kg for s = 1:m; %个体有4%的变异概率 for j = 1:n for i = 1:m if rand(1)<0.04 pop(i,j) = rands(1); end end end %r1,r2为粒子群算法参数 r1 = rand(1); r2 = rand(1); %个体和速度更新 V(s,:) = w*V(s,:) + c1*r1*(ibest(s,:)-pop(s,:)) + c2*r2*(gbest-pop(s,:)); pop(s,:) = pop(s,:) + 0.3*V(s,:); for j = 1:3 if pop(s,j) < MinX(j) pop(s,j) = MinX(j); end if pop(s,j) > MaxX(j) pop(s,j) = MaxX(j); end end for j = 4:9 if pop(s,j) < MinX(j) pop(s,j) = MinX(j); end if pop(s,j) > MaxX(j) pop(s,j) = MaxX(j); end end for j = 10:12 if pop(s,j) < MinX(j) pop(s,j) = MinX(j); end if pop(s,j) > MaxX(j) pop(s,j) = MaxX(j); end end %求更新后的每个个体适应度值 [pop(s,:),BsJ] = chap10_3b(pop(s,:),BsJ); error(s) = BsJ; %根据适应度值对个体最优和群体最优进行更新 if error(s)<Error(s) ibest(s,:) = pop(s,:); Error(s) = error(s); end if error(s)<Errorleast gbest = pop(s,:); Errorleast = error(s); end end Best(kg) = Errorleast; end plot(Best); save pfile1 gbest;