BACK PROPOGATION of errors is a learning rule which is used in
connectionist network processes. Connectionist networks are a
relatively new phenomenon which demonstrate the programs learning
ability through the production of specific outputs in response to
certain inputs given. These networks are made up of elementary units
which are connected together in order for the units to link to one
another. These units act on one another by either sending excitory or
inhibitory signals and this is their means of communication. These
networks do not have to follow explicit rules;in fact they can model
cognitive behaviour.Patterns of activation can be stored in the network
and these associate various inputs with certain outputs. Models include
many layers which deal with complex behaviour. One layer is made up of
input units which encode a stimulus for pattern activation and the
other layer produces a response to the stimulus. The networks produce
specific outputs in response to certain stimuli and because of this,it
seems that a certain behaviour is produced which demonstrates the
networks ability to follow learned rules. Networks are able to learn
association between different inputs and outputs by adjusting weights
in links between the units. Many rules can modify these weights and
this is where back propogation plays it's part. At the very beginning
of the "learning" process ,random weights are introduced on links
between units and often the response produced is not correct. Back
propogation compares the incorrect pattern with the required output
response. The errors that occur are recorded and then B.P.influences
the network so weights are modified in order for the required output to
be produced;this supervised learning technique strengthens connections
and weakens them when they are wrong;so after a certain learning
period,the required response is performed due to influence of B.P.
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