demorb3.m

神经网络学习过程的实例程序

%% Radial Basis Underlapping Neurons
% A radial basis network is trained to respond to specific inputs with target
% outputs.  However, because the spread of the radial basis neurons is too low,
% the network requires many neurons.
% 
% Copyright 1992-2002 The MathWorks, Inc.
% $Revision: 1.14 $  $Date: 2002/04/14 21:28:18 $

%%
% Define 21 inputs P and associated targets T.

P = -1:.1:1;
T = [-.9602 -.5770 -.0729  .3771  .6405  .6600  .4609 ...
      .1336 -.2013 -.4344 -.5000 -.3930 -.1647  .0988 ...
      .3072  .3960  .3449  .1816 -.0312 -.2189 -.3201];
plot(P,T,'+');
title('Training Vectors');
xlabel('Input Vector P');
ylabel('Target Vector T');


%%
% The function NEWRB quickly creates a radial basis network which approximates
% the function defined by P and T.  In addition to the training set and targets,
% NEWRB takes two arguments, the sum-squared error goal and the spread constant.
% The spread of the radial basis neurons B is set to a very small number.

eg = 0.02; % sum-squared error goal
sc = .01;  % spread constant
net = newrb(P,T,eg,sc);

%%
% To check that the network fits the function in a smooth way, define another
% set of test input vectors and simulate the network with these new inputs.  Plot
% the results on the same graph as the training set.  The test vectors reveal
% that the function has been overfit!  The network could have done better with a
% higher spread constant.

X=-1:.01:1;
Y=sim(net,X);
hold on;
plot(X,Y);
hold off;