| dc.description.abstract | Within pattern recognition, Artificial Neural Networks (ANNs) are very powerful tools used
to classify data according to patterns which may or may not be apparent within the data
itself. This ability to link pieces of data allows for trained networks to have predictive
abilities based upon new data, which can allow for advance warning of potential hazards,
such as the appearance of tumorous growths within the body.
However, this power comes at a cost of the design and architecture of the network itself,
since the capabilities are directly linked to the amount of processing power (the number of
neurons) available. Too few neurons within the network, and it will be unable to find a
suitable pattern. Too many neurons, and the network will over-fit the data, effectively
memorising the training data and being unable to generalise the pattern. This project
explored the effect of the complexity of data on the architecture of the network used to
predict it, with a view to minimise training time on real data (which can take additional time
to learn) by creating architectures on simplified, simulation data. A link was established
between input complexity and architecture, assuming the format of the data remains
unchanged, however while the link exists, it does not affect the architecture greatly. | en_GB |
