nazliii

مثلا اینطوری مینویسن

% show network
view(net)
figure;
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:.01:2;
[P1,P2] = meshgrid(span,span);
pp = [P1( P2(]';

% simulate neural network on a grid
aa = net(pp);
aa = full(aa);

% plot classification regions
figure(1)
mesh(P1,P2,reshape(aa,length(span),length(span))-5);
colormap cool
view(2)

یا اینطوری

[net,Y,E] = adapt(net,p,p);

% view network structure
view(net)



% check final network parameters
disp('Weights and bias of the ADAPTIVE after adaptation')
net.IW{1}
net.b{1}
%Weights and bias of the ADAPTIVE after adaptation

%Plot results

% transform result vectors
Y = seq2con(Y); Y = Y{1};
E = seq2con(E); E = E{1};
% start a new figure
figure;

% first graph
subplot(211)
plot(t,y,'b', t,Y,'r--');
legend('Original','Prediction')
grid on
xlabel('Time [sec]');
ylabel('Target Signal');
ylim([-1.2 1.2])

% second graph
subplot(212)
plot(t,E,'g');
grid on
legend('Prediction error')
xlabel('Time [sec]');
ylabel('Error');
ylim([-1.2 1.2])