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perceptron

Syntax

perceptron(hardlimitTF,perceptronLF)

Synopsis

Description

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

Perceptrons are provide for historical interest. For much better results use patternnet, which can solve non-linearly separable problems. Sometimes when people refer to perceptrons they are referring to feed-forward pattern recognition networks, such as patternnet. But the original perceptron, described here, can solve only very simple problems.

Perceptrons can learn to solve a narrow class of classification problems. Their significance is they have a simple learning rule and were one of the first neural networks to reliably solve a given class of problems.

perceptron(hardlimitTF,perceptronLF) takes these arguments,

hardlimitTF

Hard limit transfer function (default = 'hardlim')

perceptronLF

Perceptron learning rule (default = 'learnp')

and returns a perceptron.

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

Examples

Here a perceptron is used to solve a very 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);
view(net)
y = net(x);

See Also

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