demosm2.m

神经网络学习过程的实例程序

%% A Two-dimensional Self-organizing Map
% As in DEMOSM1, this self-organizing map will learn to represent different
% regions of the input space where input vectors occur.  In this demo, however,
% the neurons will arrange themselves in a two-dimensional grid, rather than a
% line.
%
% Copyright 1992-2002 The MathWorks, Inc. 
% $Revision: 1.18 $  $Date: 2002/04/14 21:28:33 $

%%
% We would like to classify 1000 two-element vectors occuring in a rectangular
% shaped vector space.

P = rands(2,1000);
plot(P(1,:),P(2,:),'+r')

%%
% We will use a 5 by 6 layer of neurons to classify the vectors above. We would
% like each neuron to respond to a different region of the rectangle, and
% neighboring neurons to respond to adjacent regions.  We create a layer of 30
% neurons spread out in a 5 by 6 grid:

net = newsom([0 1; 0 1],[5 6]);

%%
% We can visualize the network we have just created with PLOTSOM.
% 
% Each neuron is represented by a red dot at the location of its two weights.
% Initially all the neurons have the same weights in the middle of the vectors,
% so only one dot appears.

plotsom(net.iw{1,1},net.layers{1}.distances)

%%
% Now we train the map on the 1000 vectors for 1 epoch and replot the network
% weights.
% 
% After training, note that the layer of neurons has begun to self-organize so
% that each neuron now classifies a different region of the input space, and
% adjacent (connected) neurons respond to adjacent regions.

net.trainParam.epochs = 1;
net = train(net,P);
plotsom(net.iw{1,1},net.layers{1}.distances)

%%
% We can now use SIM to classify vectors by giving them to the network and
% seeing which neuron responds.
% 
% The neuron indicated by "a" responded with a "1", so p belongs to that class.

p = [0.5;0.3];
a = sim(net,p)