SingleLayer Perceptron Neural Networks
A singlelayer perceptron network consists of one or more artificial neurons
in parallel. The neurons may be of the same type we've seen in the
Artificial Neuron Applet.

Each neuron in the layer provides one network output, and is usually connected
to all of the external (or environmental) inputs.

The applet in this tutorial is an example of a singleneuron, singlelayer
perceptron network, with just two inputs.
The perceptron learning rule, which we study next, provides a simple algorithm
for training a perceptron neural network. However, as we will see, singlelayer
perceptron networks cannot learn everything: they are not computationally
complete. As mentioned in the introduction, twoinput networks cannot approximate
the XOR (or XNOR) functions. Of the (2^{2})^{n} or 16 possible
functions, a twoinput perceptron can only perform 14 functions. As the
number of inputs, n, increases, the proportion of functions that can be
computed decreases rapidly.
Later, we will investigate multilayer perceptrons.
[Back to the Simple Perceptron
Learning applet page ]