看了程序 我都看糊涂了  我不知道哪里是数据的输入

求大牛指导一下啊

PSO优化的

%用粒子群算法优化RBF网络权值

clear all

close all

G =250;   %迭代次数

n = 12;   %粒子维数

m = 20;   %种群规模

w = 0.1;  %算法参数

c1 = 2;   %算法参数

c2 = 2;   %算法参数

%取粒子的取值范围

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

%初始化种群pop

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

%初始化粒子速度

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,:);    %抽出个体

[indivi,BsJ] = fitness(indivi,BsJ);   %求出每个粒子对应的误差

Error(s) = BsJ;

end

[OderEr,IndexEr] = sort(Error);    %对误差进行排序

Error;

Errorleast = OderEr(1);    %求出最小误差

for i = 1:m

if Errorleast == Error(i)

gbest = pop(i,:);   %找出最小误差对应的个体极值gbest

break;

end

end

ibest = pop;   %把初始化的种群作为群体极值

%循环开始

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);  %对个体pop(i,j)进行变异

end

end

end

%r1,r2为粒子群算法参数

r1 = rand(1);

r2 = rand(1);

% 速度更新

V(s,:) = w*V(s,:) + c1*r1*(ibest(s,:)-pop(s,:)) + c2*r2*(gbest-pop(s,:));

%个体更新

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误差，可看成适应度值

[pop(s,:),BsJ] = fitness(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)

ibest(s,:) = pop(s,:);

Error(s) = error(s);

end

if error(s)

gbest = pop(s,:);

Errorleast = error(s);

end

end

Best(kg) = Errorleast;

end

plot(Best);

title('遗传算法优化RBF网络权值中最小误差进化过程')

xlabel('进化次数');

ylabel('最小误差');

save pfile1 gbest;

GA优化的

clear all

close all

%遗传算法优化来训练RBF网络权值

%G为进化代数,Size为种群规模,CodeL为参数的二进制编码长度

G = 250;

Size = 30;

CodeL = 10;

%确定每个参数的最大最小值

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

%初始化种群

E = round(rand(Size,12*CodeL));

BsJ = 0;

%进化开始

for kg = 1:1:G

time(kg) = kg

for s = 1:1:Size

m = E(s,:);    %取出其中个体

%把二进制表示的参数转化为实数

for j = 1:1:12

y(j) = 0;

mj = m((j-1)*CodeL + 1:1:j*CodeL);

for i = 1:1:CodeL

y(j) = y(j) + mj(i)*2^(i - 1);

end

f(s,j) = (MaxX(j) - MinX(j))*y(j)/1023 + MinX(j);

end

p = f(s,:);

[p,BsJ] = fitness(p,BsJ);

BsJi(s) = BsJ;             %记录每个个体的总误差

end

%对误差排序，求出最好误差

[OderJi,IndexJi] = sort(BsJi);

BestJ(kg) = OderJi(1);

BJ = BestJ(kg);

Ji = BsJi + 1e-10;

%对误差取倒数，求出适应度值

fi = 1./Ji;    %适应度值

[Oderfi,Indexfi] = sort(fi);

Bestfi = Oderfi(Size);      %最佳适应度值

BestS = E(Indexfi(Size),:);     %最佳个体

kg  %进化次数

p    %最佳个体

BJ   %最佳个体的误差

%**************Step 2:选择操作**********************%

fi_sum = sum(fi);

fi_Size = (Oderfi/fi_sum)*Size;

fi_S = floor(fi_Size);

kk = 1;

for i = 1:1:Size

for j = 1:1:fi_S(i)

TempE(kk,:) = E(Indexfi(i),:);

kk = kk + 1;

end

end

%***************Step 3:交叉操作***********************************%

pc = 0.60;

n = ceil(20*rand);

for i = 1:2:(Size-1)

temp = rand;

if pc>temp

for j = n:1:20

TempE(i,j) = E(i+1,j);

TempE(i+1,j) = E(i,j);

end

end

end

TempE(Size,:) = BestS;

E = TempE;

%***************Step 4:变异操作**********************************%

pm = 0.001 - [1:1:Size]*(0.001)/Size;

for i = 1:1:Size

for j = 1:1:12*CodeL

temp = rand;

if pm>temp

if TempE(i,j) == 0

TempE(i,j) = 1;

else

TempE(i,j) = 0;

end

end

end

end

%把最佳个体赋于种群中

TempE(Size,:) = BestS;

E = TempE;

end

Bestfi

BestS

fi

Best_J = BestJ(G)

figure(1)

plot(time,BestJ);

title('遗传算法优化RBF网络权值中最小误差进化过程')

xlabel('进化次数');

ylabel('最小误差');

save pfile p;

测试的程序

clear all

close all

%分别使用粒子群算法，遗传算法和未经过优化权值的RBF网络做预测

%

load pfile1 gbest;   %粒子群算法优化得到权值

load pfile p;        %遗传算法优化得到权值

%学习系数

alfa = 0.05;

xite = 0.85;

x = [0,0]';

for M=1:3

if M==1   %取粒子群算法进化的权值

b=[gbest(1);gbest(2);gbest(3)];

c=[gbest(4) gbest(5) gbest(6);

gbest(7) gbest(8) gbest(9)];

w=[gbest(10);gbest(11);gbest(12)];

elseif M==2   %取遗传算法进化的权值

b=[p(1);p(2);p(3)];

c=[p(4) p(5) p(6);

p(7) p(8) p(9)];

w=[p(10);p(11);p(12)];

elseif M==3   %权值重新初始化

b=3*rand(3,1);

c=3*rands(2,3);

w=rands(3,1);

end

w_1 = w;w_2 = w_1;

c_1 = c;c_2 = c_1;

b_1 = b;b_2 = b_1;

y_1 = 0;

ts = 0.001;

for k = 1:1:1500

time(k) = k*ts;

%RBF网络的输入，控制量和系统上一个输入量

u(k) = sin(5*2*pi*k*ts);

y(k) = u(k)^3 + y_1/(1 + y_1^2);

x(1) = u(k);

x(2) = y(k);

%网络预测的输入

for j = 1:1:3

h(j) = exp(-norm(x - c(:,j))^2/(2*b(j)*b(j)));

end

ym(M,k) = w_1'*h';

%预测输出和实际输出的误差

e(M,k) = y(k) - ym(M,k);

%调整权值

d_w = 0*w;d_b = 0*b;d_c = 0*c;

for j = 1:1:3

d_w(j) = xite*e(M,k)*h(j);

d_b(j) = xite*e(M,k)*w(j)*h(j)*(b(j)^-3)*norm(x-c(:,j))^2;

for i = 1:1:2

d_c(i,j) = xite*e(M,k)*w(j)*h(j)*(x(i) - c(i,j))*(b(j)^-2);

end

end

w = w_1 + d_w + alfa*(w_1 - w_2);

b = b_1 + d_b + alfa*(b_1 - b_2);

c = c_1 + d_c + alfa*(c_1 - c_2);

y_1 = y(k);

w_2 = w_1;

w_1 = w;

c_2 = c_1;

c_1 = c;

b_2 = b_1;

b_1 = b;

end

end

figure(1)

plot(e(1,:));

hold on

plot(e(2,:),'r');

hold on

plot(e(3,:),'g');

title('各种算法对应的预测误差')

legend('PSO_RBF优化误差','GA_RBF优化误差','RBF优化误差')

xlabel('进化次数');

ylabel('预测误差');

figure(2)

plot(y,'y');

hold on

plot(ym(1,:),'b');

hold on

plot(ym(2,:),'r');

hold on

plot(ym(3,:),'g');

title('各种算法对应的系统预测输出')

legend('实际输出','PSO_RBF预测输出','GA_RBF预测输出','RBF预测输出')

xlabel('进化次数');

ylabel('预测误差');

2012-9-5 22:50 上传

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