|dc.description.abstract||Artificial neural networks (ANNs) were originally inspired by networks of biological neurons
and the interactions present in networks of these neurons. The recent revival of interest in ANNs has again focused attention on the apparent ability of ANNs to solve difficult problems,
such as machine vision, in novel ways.
There are many types of ANNs which differ in architecture and learning algorithms, and the
list grows annually. This study was restricted to feed-forward architectures and Backpropagation-
like (BP-like) learning algorithms. However, it is well known that the learning problem
for such networks is NP-complete. Thus generative and incremental learning algorithms,
which have various advantages and to which the NP-completeness analysis used for BP-like
networks may not apply, were also studied.
Various algorithms were investigated and the performance compared. Finally, the better
algorithms were applied to a number of problems including music composition, image
binarization and navigation and goal satisfaction in an artificial environment. These tasks
were chosen to investigate different aspects of ANN behaviour. The results, where appropriate,
were compared to those resulting from non-ANN methods, and varied from poor to very