
Perceptron Learning Algorithm
The perceptron learning rule was originally developed by Frank
Rosenblatt in the late 1950s. Training patterns are presented
to the network's inputs; the output is computed. Then the
connection weights w_{j}are modified by an amount
that is proportional to the product of
 the difference between the actual output, y, and
the desired output, d, and
 the input pattern, x.
The algorithm is as follows:
 Initialize the weights and threshold to small random
numbers.
 Present a vector x to the neuron inputs and calculate
the output.
 Update the weights according to:

where
 d is the desired output,
 t is the iteration number, and
 eta is the gain or step size, where 0.0 < n <
1.0
 Repeat steps 2 and 3 until:

 the iteration error is less than a userspecified error
threshold or
 a predetermined number of iterations have been
completed.
Notice that learning only occurs when an error is made, otherwise
the weights are left unchanged.
This rule is thus a modified form of Hebb learning.
During training, it is often useful to measure the performance
of the network as it attempts to find the optimal weight set. A
common error measure or cost function used is sumsquared
error. It is computed over all of the input vector/output vector
pairs in the training set and is given by the equation below:
where p is the number of input/output vector pairs in the training
set.
[Back to the Simple Perceptron
Learning applet page ]
