%定義存儲在網絡中的目標平衡點
T = [+1 -1;-1 +1]';
plot(T(1,:),T(2,:),'r*');
axis([-1.1 1.1 -1.1 1.1]);
title('Hopfield神經網絡狀態空間');
xlabel('a(1)');
ylabel('a(2)');
%建立網絡，并得到權重和閾值
net=newhop(T);
W=net.LW{1,1}
b=net.b{1,1}
%使用原始平衡點仿真網絡
[Y,Pf,Af] = sim(net,2,[],T);
Y
%使用一個隨機點仿真網絡，并繪出其到達穩定點的軌跡
a = {rands(2,1)};
[y,Pf,Af] = sim(net,{1 20},{},a);
figure;
plot(T(1,:),T(2,:),'r*');
axis([-1.1 1.1 -1.1 1.1]);
title('Hopfield神經網絡狀態空間');
xlabel('a(1)');
ylabel('a(2)');
record=[cell2mat(a) cell2mat(y)];
start=cell2mat(a);
hold on
plot(start(1,1),start(2,1),'bx',record(1,:),record(2,:));
%用多個隨機點仿真網絡，并繪出相應的軌跡
figure;
color = 'rgbmy';
axis([-1.1 1.1 -1.1 1.1]);
title('Hopfield神經網絡狀態空間');
xlabel('a(1)');
ylabel('a(2)');
hold on
for i=1:25
   a = {rands(2,1)};
   [y,Pf,Af] = sim(net,{1 20},{},a);
   record=[cell2mat(a) cell2mat(y)];
   start=cell2mat(a);
   plot(start(1,1),start(2,1),'kx',record(1,:),record(2,:),color(rem(i,5)+1));
   drawnow
end;
%給網絡指定特殊初始權重進行仿真，得到相應的軌跡
figure;
plot(T(1,:),T(2,:),'r*');
axis([-1.1 1.1 -1.1 1.1]);
title('Hopfield神經網絡狀態空間');
xlabel('a(1)');
ylabel('a(2)');
hold on
plot(0,0,'ko');
P = [-1.0 -0.5 0.0 +0.5 +1.0;  -1.0 -0.5 0.0 +0.5 +1.0];
color = 'rgbmy';
for i=1:5
   a = {P(:,i)};
   [y,Pf,Af] = sim(net,{1 50},{},a);
   record=[cell2mat(a) cell2mat(y)];  
   start=cell2mat(a);
   plot(start(1,1),start(2,1),'kx',record(1,:),record(2,:),color(rem(i,5)+1))
   drawnow
end