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Perceptrons are simple single-layer binary classifiers, which divide the input space with a linear decision boundary.

Perceptrons can learn to solve a narrow range of classification problems. They were one of the first neural networks to reliably solve a given class of problem, and their advantage is a simple learning rule.

perceptron(hardlimitTF,perceptronLF) takes these arguments,


Hard limit transfer function (default = 'hardlim')


Perceptron learning rule (default = 'learnp')

and returns a perceptron.

In addition to the default hard limit transfer function, perceptrons can be created with the hardlims transfer function. The other option for the perceptron learning rule is learnpn.

    Note   Neural Network Toolbox™ supports perceptrons for historical interest. For better results, you should instead use patternnet, which can solve nonlinearly separable problems. Sometimes the term "perceptrons" refers to feed-forward pattern recognition networks; but the original perceptron, described here, can solve only simple problems.


Solve Simple Classification Problem Using Perceptron

Use a perceptron to solve a simple classification logical-OR problem.

x = [0 0 1 1; 0 1 0 1];
t = [0 1 1 1];
net = perceptron;
net = train(net,x,t);
y = net(x);

Introduced in R2010b

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