NEURAL NETWORK ARCHITECTURES. EXAMPLES using MATLAB by J. Smith

Author:J. Smith [Smith, J.]
Language: eng
Format: azw3
Publisher: UNKNOWN
Published: 2017-02-24T05:00:00+00:00

XX = repmat(con2seq(X),1,3);

TT = repmat(con2seq(T),1,3);

net = adapt(net,XX,TT);

plotpc(net.IW{1},net.b{1});

Now SIM is used to classify any other input vector, like [0.7; 1.2]. A plot of this new point with the original training set shows how the network performs. To distinguish it from the training set, color it red.

x = [0.7; 1.2];

y = net(x);

plotpv(x,y);

point = findobj(gca,'type','line');

point.Color = 'red';

Turn on "hold" so the previous plot is not erased and plot the training set and the classification line.

The perceptron correctly classified our new point (in red) as category "zero" (represented by a circle) and not a "one" (represented by a plus).

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.