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 wjare
modified by an amount that is proportional to the product of
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the difference between the actual output, y, and the desired
output, d, and
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the input pattern, x.
The algorithm is as follows:
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Initialize the weights and threshold to small random numbers.
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Present a vector x to the neuron inputs and calculate the output.
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Update the weights according to:
where
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d is the desired output,
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t is the iteration number, and
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eta is the gain or step size, where 0.0 < n < 1.0
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Repeat steps 2 and 3 until:
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the iteration error is less than a user-specified error threshold or
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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 sum-squared 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 ]
