demogrn1.m

神经网络学习过程的实例程序

%% GRNN Function Approximation
% This demonstration uses functions NEWGRNN and SIM.
%
% Copyright 1992-2002 The MathWorks, Inc.
% $Revision: 1.7 $  $Date: 2002/04/14 21:26:43 $

%%
% Here are eight data points of a function we would like to fit.  The functions
% inputs P should result in target outputs T.

P = [1 2 3 4 5 6 7 8];
T = [0 1 2 3 2 1 2 1];

plot(P,T,'.','markersize',30)
axis([0 9 -1 4])
title('Function to approximate.')
xlabel('P')
ylabel('T')


%%
% We use NEWGRNN to create a generalized regression network.  We use a SPREAD
% slightly lower than 1, the distance between input values, in order, to get a
% function that fits individual data points fairly closely.  A smaller spread
% would fit data better but be less smooth.

spread = 0.7;
net = newgrnn(P,T,spread);
A = sim(net,P);

hold on
outputline = plot(P,A,'.','markersize',30,'color',[1 0 0]);
title('Create and test a network.')
xlabel('P')
ylabel('T and A')

%%
% We can use the network to approximate the function at a new input value.

p = 3.5;
a = sim(net,p);
plot(p,a,'.','markersize',30,'color',[1 0 0]);
title('New input value.')
xlabel('P and p')
ylabel('T and a')


%%
% Here the network's response is simulated for many values, allowing us to see
% the function it represents.

P2 = 0:.1:9;
A2 = sim(net,P2);
plot(P2,A2,'linewidth',4,'color',[1 0 0])
title('Function to approximate.')
xlabel('P and P2')
ylabel('T and A2')