rbf.m

我们上课的资料

%% 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: 2002/03/29 19:36:06 $

%%
% 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('训练样本');
xlabel('输入向量 P');
ylabel('目标向量 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.

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('输入');

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

hold on;
plot(X,Y);
hold off;
legend({'目标','输出'})