📄 applin1.m
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%APPLIN1 Linear prediction.
% Mark Beale, 12-15-93
% Copyright 1992-2002 The MathWorks, Inc.
% $Revision: 1.14 $ $Date: 2002/04/14 21:22:30 $
clf;
figure(gcf)
echo on
% NEWLIND - Solves for a linear layer.
% SIM - Simulates a linear layer.
% LINEAR PREDICTION:
% Using the above functions a linear neuron is designed
% to predict the next value in a signal, given the last
% five values of the signal.
pause % Strike any key to continue...
% DEFINING A WAVE FORM
% ====================
% TIME defines the time steps of this simulation.
time = 0:0.025:5; % from 0 to 6 seconds
% T defines the signal in time to be predicted:
T = sin(time*4*pi);
Q = length(T);
% The input P to the network is the last five values
% of the signal T:
P = zeros(5,Q);
P(1,2:Q) = T(1,1:(Q-1));
P(2,3:Q) = T(1,1:(Q-2));
P(3,4:Q) = T(1,1:(Q-3));
P(4,5:Q) = T(1,1:(Q-4));
P(5,6:Q) = T(1,1:(Q-5));
pause % Strike any key to see these signals...
% PLOTTING THE SIGNALS
% ====================
% Here is a plot of the signal to be predicted:
plot(time,T)
xlabel('Time');
ylabel('Target Signal');
title('Signal to be Predicted');
pause % Strike any key to design the network...
% NEWLIND solves for weights and biases which will let
% the linear neuron model the system.
net = newlind(P,T);
pause % Strike any key to test the predictor...
% TESTING THE PREDICTOR
% =====================
% SIM simulates the linear neuron which attempts
% to predict the next value in the signal at each
% timestep.
a = sim(net,P);
% The output signal is plotted with the targets.
plot(time,a,time,T,'+')
xlabel('Time');
ylabel('Output - Target +');
title('Output and Target Signals');
% The linear neuron does a good job.
pause % Strike any key to see the error signal...
% Error is the difference between output and target signals.
e = T - a;
% This error can be plotted.
plot(time,e)
hold on
plot([min(time) max(time)],[0 0],':r')
hold off
xlabel('Time');
ylabel('Error');
title('Error Signal');
% Notice how small the error is!
echo off
disp('End of APPLIN1')
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