hopfiel.m

考虑3个神经元的hopfield网络

% 考虑3个神经元的hopfield网络，每个神经元有一个阀值和一个权重，定义存储在网络中的目标平衡点中为矩阵T的两个三
 
%指定存储在网络中的目标平衡点 
T = [+1 +1;-1 +1; -1 -1]; 
axis([-1 1 -1 1 -1 1]); 
set(gca,'box','on'); 
axis manual; 
hold on; 
plot3(T(1,:),T(2,:),T(3,:),'r*'); 
title('Hopfield神经网络状态空间'); 
xlabel('a(1)'); 
ylabel('a(2)'); 
zlabel('a(3)'); 
view([37.5 30]); 
%建立网络，并得到权重和阈值 
net=newhop(T); 
W=net.LW{1,1};
b=net.b{1,1};
%使用一个随机点仿真网络，并绘出其到达稳定点的轨迹 
a = {rands(3,1)}; 
[y,Pf,Af] = sim(net,{1 10},{},a); 
figure; 
axis([-1 1 -1 1 -1 1]); 
set(gca,'box','on'); 
axis manual; 
hold on; 
plot3(T(1,:),T(2,:),T(3,:),'r*'); 
record=[cell2mat(a) cell2mat(y)]; 
start=cell2mat(a); 
plot3(start(1,1),start(2,1),start(3,1),'bx',record(1,:),record(2,:),record(3,:)); 
view([37.5 30]);
title('Hopfield神经网络状态空间'); 
xlabel('a(1)'); 
ylabel('a(2)'); 
zlabel('a(3)'); 
%用多个随机点仿真网络，并绘出相应的轨迹 
figure; 
axis([-1 1 -1 1 -1 1]); 
set(gca,'box','on'); 
axis manual; 
hold on; 
view([37.5 30]); 
plot3(T(1,:),T(2,:),T(3,:),'r*'); 
color = 'rgbmy'; 
for i=1:25 
	 a = {rands(3,1)}; 
	[y,Pf,Af] = sim(net,{1 10},{},a); 
	record=[cell2mat(a) cell2mat(y)]; 
	start=cell2mat(a); 
	plot3(start(1,1),start(2,1),start(3,1),'kx',record(1,:),record(2,:),record(3,:),color(rem(i,5)+1)) 
	drawnow 
end 
title('Hopfield神经网络状态空间'); 
xlabel('a(1)'); 
ylabel('a(2)'); 
zlabel('a(3)');