ex1d2.m

新的神经网络算法源程序

%==============================================================
%    Ex1d2: An example for using WaveNet,  1-D Interpolation
%==============================================================

% By Qinghua Zhang. March, 1994.

help ex1d2

echo on

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

% Input patterns
x = (-1:.05:1)';

% Output patterns
y = (-2.186 * x - 1.2864) .* (x < -0.2) ...
    + 4.246 * x             .* ((x >= -0.2) & (x < 0)) ...
    + (exp(-0.5 .* x - 0.5) .* sin((3 .* x + 7) .* x)) .* (x > 0);

% The data are depicted in the graphics window.

clf; plot(x,y,'o'); 
xlabel('x'); ylabel('y'); title('The data to be interpolated');

% We are going to use a WaveNet to interpolate the data.
% You may set the number of wavelets in the network to 9
% 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, 4, 3, 1, 5); 
  % Use 9 wavelets, 4 iterations, initialization mode 3,
  % minimum number of covered data points 1, scanned levels 5.
else
  th = wnetreg(y, x, [], 4, 3, 1, 5); 
end

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

% Evaluate the network on a finer x-sequence

xg = (-1:.01:1)';
yg = wavenet(xg, th);

plot(x,y,'o', xg,yg); xlabel('x'); ylabel('y');

% The result of the interpolation is ploted in the graphics window.

echo off