پیاده سازی گیت های OR ، AND و XOR با پرسپترون

کد دروازه منطقی AND :

clear all

close all

clc

w1 = 0; w2 = 0; b = 0;

s = [1 1 1; 1 -1 1; -1 1 1; -1 -1 1];

t=[1 -1 -1 -1];

alfa = 0.9;

%%lerning

for i=1:1:4

  X1=s(i,1); X2=s(i,2); y=t(1,i);

   w1new=w1+X1*alfa*y; w2new=w2+X2*alfa*y; bnew=b+alfa*1*y;

   X=-5:0.05:5;

   yin = b+w1*X1+w2*X2

   if yin>=0

      disp('yin = 1')

      else y < 0

     disp('yin = -1')  

   end

   x=-(w1new/w2new)*X-((1/w2new)*bnew);

   figure , plot(x,X,'r','linewidth',3)

   hold on

  plot(X,0,'g')

  plot(0,X,'g')

   w1=w1new; 

   w2=w2new; 

  b=bnew;  

  plot(1,1,'+','linewidth',2)

hold on

plot(1,-1,'d','linewidth',2)

hold on

plot(-1,1,'d','linewidth',2)

hold on

plot(-1,-1,'d','linewidth',2)

hold on

title('heb for AND');

end

%%testing

X1=input('insert x1   ')

X2=input('insert x2   ')

yin = b+w1*X1+w2*X2

   if yin>=0

      disp('yin = 1')

      else y < 0

     disp('yin = -1')  

     end

کد دروازه منطقی OR : 

clear all

close all

clc

w1 = 0; w2 = 0; b = 0;

s = [1 1 1; 1 -1 1; -1 1 1; -1 -1 1];

t=[1 1 1 -1];

alfa = 0.9;

%%lerning

for i=1:1:4

  X1=s(i,1); X2=s(i,2); y=t(1,i);

   w1new=w1+X1*alfa*y; w2new=w2+X2*alfa*y; bnew=b+alfa*1*y;

   X=-5:0.05:5;

   yin = b+w1*X1+w2*X2

   if yin>=0

      disp('yin = 1')

      else y < 0

     disp('yin = -1')  

   end

   x=-(w1new/w2new)*X-((1/w2new)*bnew);

   figure , plot(x,X,'r','linewidth',3)

   hold on

  plot(X,0,'g')

  plot(0,X,'g')

   w1=w1new; 

   w2=w2new; 

  b=bnew;  

  plot(1,1,'+','linewidth',2)

hold on

plot(1,-1,'+','linewidth',2)

hold on

plot(-1,1,'+','linewidth',2)

hold on

plot(-1,-1,'d','linewidth',2)

hold on

title('heb for OR');

end

%%testing

X1=input('insert x1   ')

X2=input('insert x2   ')

yin = b+w1*X1+w2*X2

   if yin>=0

      disp('yin = 1')

      else y < 0

     disp('yin = -1')  

   end

کد دروازه منطقی XOR : 

close all, clear all, clc, format compact

% number of samples of each class

K = 100;

% define 4 clusters of input data

q = .6; % offset of classes

A = [rand(1,K)-q; rand(1,K)+q];

B = [rand(1,K)+q; rand(1,K)+q];

C = [rand(1,K)+q; rand(1,K)-q];

D = [rand(1,K)-q; rand(1,K)-q];

% plot clusters

figure(1)

plot(A(1,:),A(2,:),'k+')

hold on

grid on

plot(B(1,:),B(2,:),'bd')

plot(C(1,:),C(2,:),'k+')

plot(D(1,:),D(2,:),'bd')

% text labels for clusters

text(.5-q,.5+2*q,'Class A')

text(.5+q,.5+2*q,'Class B')

text(.5+q,.5-2*q,'Class A')

text(.5-q,.5-2*q,'Class B')

% encode clusters a and c as one class, and b and d as another class

a = -1; % a | b

c = -1; % -------

b = 1; % d | c

d = 1; %

% define inputs (combine samples from all four classes)

P = [A B C D];

% define targets

T = [repmat(a,1,length(A)) repmat(b,1,length(B)) ...

 repmat(c,1,length(C)) repmat(d,1,length(D)) ];

% view inputs |outputs

%[P' T']

% create a neural network

net = feedforwardnet([5 3]);

% train net

net.divideParam.trainRatio = 1; % training set [%]

net.divideParam.valRatio = 0; % validation set [%]

net.divideParam.testRatio = 0; % test set [%]

% train a neural network

[net,tr,Y,E] = train(net,P,T);

% show network

view(net)

figure(2)

plot(T','linewidth',2)

hold on

plot(Y','r--')

grid on

legend('Targets','Network response','location','best')

ylim([-1.25 1.25])

% generate a grid

span = -1:.005:2;

[P1,P2] = meshgrid(span,span);

pp = [P1(:) P2(:)]';

% simulate neural network on a grid

aa = net(pp);

% translate output into [-1,1]

%aa = -1 + 2*(aa>0);

% plot classification regions

figure(1)

mesh(P1,P2,reshape(aa,length(span),length(span))-5);

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