Single-Layer Perceptron Neural Networks
A single-layer 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.
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Each neuron in the layer provides one network output, and is usually connected
to all of the external (or environmental) inputs.
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The applet in this tutorial is an example of a single-neuron, single-layer
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, single-layer
perceptron networks cannot learn everything: they are not computationally
complete. As mentioned in the introduction, two-input networks cannot approximate
the XOR (or XNOR) functions. Of the (22)n or 16 possible
functions, a two-input 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 ]