demorb1.m

神经网络学习过程的实例程序

%% Radial Basis Approximation
% This demo uses the NEWRB function to create a radial basis network that
% approximates a function defined by a set of data points.
%
% Copyright 1992-2002 The MathWorks, Inc. 
% $Revision: 1.14 $  $Date: 2002/04/14 21:28:13 $

%%
% 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');

%%
% We would like to find a function which fits the 21 data points.  One way to do
% this is with a radial basis network.  A radial basis network is a network with
% two layers.  A hidden layer of radial basis neurons and an output layer of
% linear neurons.  Here is the radial basis transfer function used by the hidden
% layer.

p = -3:.1:3;
a = radbas(p);
plot(p,a)
title('Radial Basis Transfer Function');
xlabel('Input p');
ylabel('Output a');

%%
% The weights and biases of each neuron in the hidden layer define the position
% and width of a radial basis function.  Each linear output neuron forms a
% weighted sum of these radial basis functions.  With the correct weight and
% bias values for each layer, and enough hidden neurons, a radial basis network
% can fit any function with any desired accuracy.  This is an example of three
% radial basis functions (in blue) are scaled and summed to produce a function
% (in magenta).

a2 = radbas(p-1.5);
a3 = radbas(p+2);
a4 = a + a2*1 + a3*0.5;
plot(p,a,'b-',p,a2,'b--',p,a3,'b--',p,a4,'m-')
title('Weighted Sum of Radial Basis Transfer Functions');
xlabel('Input p');
ylabel('Output a');

%%
% 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.

eg = 0.02; % sum-squared error goal
sc = 1;    % spread constant
net = newrb(P,T,eg,sc);

%%
% To see how the network performs, replot the training set.  Then simulate the
% network response for inputs over the same range.  Finally, plot the results on
% the same graph.

plot(P,T,'+');
xlabel('Input');

X = -1:.01:1;
Y = sim(net,X);

hold on;
plot(X,Y);
hold off;
legend({'Target','Output'})