粒子群优化RBF神经网络源码程序

太过爱你忘了你带给我的痛 2022-09-11 10:28 36阅读 0赞

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) = rand(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;  
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