demolin6.m

神经网络学习过程的实例程序

%% Linearly Dependent Problem
% A linear neuron is trained to find the minimum error solution for a problem
% with linearly dependent input vectors.  If a linear dependence in input
% vectors is not matched in the target vectors, the problem is nonlinear and
% does not have a zero error linear solution.
%
% Copyright 1992-2002 The MathWorks, Inc.
% $Revision: 1.12 $  $Date: 2002/04/14 21:27:28 $

%%
% P defines three 2-element input patterns (column vectors).  Note that 0.5
% times the sum of (column) vectors 1 and 3 results in vector 2.  This is called
% linear dependence.

P = [ 1.0   2.0   3.0; ...
      4.0   5.0   6.0];

%%
% T defines an associated 1-element target (column vectors).  Note that 0.5
% times the sum of -1.0 and 0.5 does not equal 1.0.  Because the linear
% dependence in P is not matched in T this problem is nonlinear and does not
% have a zero error linear solution.

T = [0.5 1.0 -1.0];

%%
% MAXLINLR finds the fastest stable learning rate for TRAINWH.  NEWLIN creates a
% linear neuron.  NEWLIN takes these arguments: 1) Rx2 matrix of min and max
% values for R input elements, 2) Number of elements in the output vector, 3)
% Input delay vector, and 4) Learning rate.

maxlr = maxlinlr(P,'bias');
net = newlin([0 10;0 10],1,[0],maxlr);

%%
% TRAIN uses the Widrow-Hoff rule to train linear networks by default.  We will
% display each 50 epochs and train for a maximum of 500 epochs.

net.trainParam.show = 50;     % Frequency of progress displays (in epochs).
net.trainParam.epochs = 500;  % Maximum number of epochs to train.
net.trainParam.goal = 0.001;  % Sum-squared error goal.

%%
% Now the network is trained on the inputs P and targets T.  Note that, due to
% the linear dependence between input vectors, the problem did not reach the
% error goal represented by the black line.

[net,tr] = train(net,P,T);

%%
% We can now test the associator with one of the original inputs, [1; 4] , and
% see if it returns the target, 0.5.  The result is not 0.5 as the linear
% network could not fit the nonlinear problem caused by the linear dependence
% between input vectors.

p = [1.0; 4];
a = sim(net,p)