ex2d.m

新的神经网络算法源程序

%=============================================================
%    Ex2d: An example for using WaveNet,  2-D Interpolation
%=============================================================

% By Qinghua Zhang. March, 1994.

help ex2d

echo on

% The function to be interpolated y=f(x1,x2)
%============================================

% A mesh surface of the function f is  drawn in the graphics window.

[x1,x2] = meshgrid(-1:0.1:1, -1:0.1:1);
xmesh = [reshape(x1, 441,1) reshape(x2, 441,1)];
ymesh = ex2d_fun(xmesh); % ymesh=f(xmesh)
ymesh = reshape(ymesh, 21,21);
clf; mesh(x1,x2,ymesh, zeros(size(ymesh))); title('y=f(x1,x2)');

pause

% The training data
%===================

x = rand(200,2)*2 - 1; % Randomly generate 200 input patterns
y = ex2d_fun(x); % y=f(x)

% The input patterns are depicted in the x1-x2 plane.

clf; plot(x(:,1), x(:,2), 'o'); title('Input Patterns in x1-x2 Plane')
xlabel('x1'); ylabel('x2');

% We are going to use a WaveNet to interpolate the data.
% You may set the number of wavelets in the network to 8
% or choose it on-line with the aid of the FPE criterion.

% Call the training procedure
%=============================

Key = keymenu('Make your choice','Use 9 wavelets','On-line choice');

if Key==1
  th = wnetreg(y, x, 9, 10, 2); 
  % Use 9 wavelets, 10 iterations, initialization mode 2.
else
  th = wnetreg(y, x, [], 10, 2); 
end

% Examine the result of the interpolation
%=========================================

% Evaluate the network on another randomly selected input patterns
xg = rand(200,2)*2 - 1; % Randomly generate 200 new input patterns
yt = ex2d_fun(xg);  % yt=f(xg);
yg = wavenet(xg, th); 

clf; plot([yt yg]); %Compare yt and yg
title('Compare yt and yg'); xlabel('Sample number');
ylabel('yt: -    yg: --');
echo off