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,
Hard limit transfer function (default = 'hardlim')
Perceptron learning rule (default = 'learnp')
and returns a perceptron.
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);