# 【MATLAB代做|FPGA代做】粒子群优化RBF神经网络

### 时间：2017-5-25 18:52:09 点击：

核心提示：粒子群优化RBF神经网络...

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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