看了程序 我都看糊涂了??我不知道哪里是數(shù)據(jù)的輸入

求大牛指導一下啊

PSO優(yōu)化的

%用粒子群算法優(yōu)化RBF網(wǎng)絡權值

clear all

close all

G =250;? ?%迭代次數(shù)

n = 12;? ?%粒子維數(shù)

m = 20;? ?%種群規(guī)模

w = 0.1;??%算法參數(shù)

c1 = 2;? ?%算法參數(shù)

c2 = 2;? ?%算法參數(shù)

%取粒子的取值范圍

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

%初始化種群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;

%根據(jù)初始化的種群計算個體好壞,找出群體最優(yōu)和個體最優(yōu)

for s = 1:m

indivi = pop(s,:);? ? %抽出個體

[indivi,BsJ] = fitness(indivi,BsJ);? ?%求出每個粒子對應的誤差

Error(s) = BsJ;

end

[OderEr,IndexEr] = sort(Error);? ? %對誤差進行排序

Error;

Errorleast = OderEr(1);? ? %求出最小誤差

for i = 1:m

if Errorleast == Error(i)

gbest = pop(i,:);? ?%找出最小誤差對應的個體極值gbest

break;

end

end

ibest = pop;? ?%把初始化的種群作為群體極值

%循環(huán)開始

for kg = 1:G

kg

for s = 1:m;

%個體有4%的變異概率

for j = 1:n

for i = 1:m

if rand(1)<0.04

pop(i,j) = rands(1);??%對個體pop(i,j)進行變異

end

end

end

%r1,r2為粒子群算法參數(shù)

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,:));

%個體更新

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

%求更新后的每個個體誤差，可看成適應度值

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

error(s) = BsJ;

%根據(jù)適應度值對個體最優(yōu)和群體最優(yōu)進行更新

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('遺傳算法優(yōu)化RBF網(wǎng)絡權值中最小誤差進化過程')

xlabel('進化次數(shù)');

ylabel('最小誤差');

save pfile1 gbest;

GA優(yōu)化的

clear all

close all

%遺傳算法優(yōu)化來訓練RBF網(wǎng)絡權值

%G為進化代數(shù),Size為種群規(guī)模,CodeL為參數(shù)的二進制編碼長度

G = 250;

Size = 30;

CodeL = 10;

%確定每個參數(shù)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

%初始化種群

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

BsJ = 0;

%進化開始

for kg = 1:1:G

time(kg) = kg

for s = 1:1:Size

m = E(s,:);? ? %取出其中個體

%把二進制表示的參數(shù)轉(zhuǎn)化為實數(shù)

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;? ?? ?? ?? ? %記錄每個個體的總誤差

end

%對誤差排序，求出最好誤差

[OderJi,IndexJi] = sort(BsJi);

BestJ(kg) = OderJi(1);

BJ = BestJ(kg);

Ji = BsJi + 1e-10;

%對誤差取倒數(shù)，求出適應度值

fi = 1./Ji;? ? %適應度值

[Oderfi,Indexfi] = sort(fi);

Bestfi = Oderfi(Size);? ?? ?%最佳適應度值

BestS = E(Indexfi(Size),:);? ???%最佳個體

kg??%進化次數(shù)

p? ? %最佳個體

BJ? ?%最佳個體的誤差

%**************Step 2:選擇操作**********************%

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:變異操作**********************************%

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

%把最佳個體賦于種群中

TempE(Size,:) = BestS;

E = TempE;

end

Bestfi

BestS

fi

Best_J = BestJ(G)

figure(1)

plot(time,BestJ);

title('遺傳算法優(yōu)化RBF網(wǎng)絡權值中最小誤差進化過程')

xlabel('進化次數(shù)');

ylabel('最小誤差');

save pfile p;

測試的程序

clear all

close all

%分別使用粒子群算法，遺傳算法和未經(jīng)過優(yōu)化權值的RBF網(wǎng)絡做預測

%

load pfile1 gbest;? ?%粒子群算法優(yōu)化得到權值

load pfile p;? ?? ???%遺傳算法優(yōu)化得到權值

%學習系數(shù)

alfa = 0.05;

xite = 0.85;

x = [0,0]';

for M=1:3

if M==1? ?%取粒子群算法進化的權值

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? ?%取遺傳算法進化的權值

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? ?%權值重新初始化

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網(wǎng)絡的輸入，控制量和系統(tǒng)上一個輸入量

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);

%網(wǎng)絡預測的輸入

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';

%預測輸出和實際輸出的誤差

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

%調(diào)整權值

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('各種算法對應的預測誤差')

legend('PSO_RBF優(yōu)化誤差','GA_RBF優(yōu)化誤差','RBF優(yōu)化誤差')

xlabel('進化次數(shù)');

ylabel('預測誤差');

figure(2)

plot(y,'y');

hold on

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

hold on

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

hold on

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

title('各種算法對應的系統(tǒng)預測輸出')

legend('實際輸出','PSO_RBF預測輸出','GA_RBF預測輸出','RBF預測輸出')

xlabel('進化次數(shù)');

ylabel('預測誤差');

2012-9-5 22:50 上傳

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