demohop2.m

神经网络学习过程的实例程序

%% A Hopfield Network with Unstable Equilibria
% A Hopfield network is designed with target stable points.  However, while
% NEWHOP finds a solution with the minimum number of unspecified stable points,
% they do often occur.  The Hopfield network designed here is shown to have an
% undesired equilibrium point.  However, these points are unstable in that any
% noise in the system will move the network out of them.
% 
% Copyright 1992-2002 The MathWorks, Inc.
% $Revision: 1.16 $  $Date: 2002/04/14 21:26:53 $

%%
% We would like to obtain a Hopfield network that has the two stable points
% define by the two target (column) vectors in T.
T = [+1 -1; ...
      -1 +1];

%%
% Here is a plot where the stable points are shown at the corners.  All possible
% states of the 2-neuron Hopfield network are contained within the plots
% boundaries.

plot(T(1,:),T(2,:),'r*')
axis([-1.1 1.1 -1.1 1.1])
title('Hopfield Network State Space')
xlabel('a(1)');
ylabel('a(2)');

%%
% The function NEWHOP creates Hopfield networks given the stable points T.

net = newhop(T);

%%
% Here we define a random starting point and simulate the Hopfield network for
% 50 steps.  It should reach one of its stable points.

a = {rands(2,1)};
[y,Pf,Af] = sim(net,{1 50},{},a);

%%
% We can make a plot of the Hopfield networks activity.
%
% Sure enough, the network ends up in either the upper-left or lower right
% corners of the plot.

record = [cell2mat(a) cell2mat(y)];
start = cell2mat(a);
hold on
plot(start(1,1),start(2,1),'bx',record(1,:),record(2,:))

%%
% Unfortunately, the network has undesired stable points at places other than
% the corners.  We can see this when we simulate the Hopfield for the
% five initial weights, P.
%
% These points are exactly between the two target stable points.  The result is
% that they all move into the center of the state space, where an undesired
% stable point exists.

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