The work presented in this thesis is mainly involved in the study of
Artificial Neural Networks (ANNs) and their learning strategies. The ANN
simulator incorporating the Backpropagation (BP) algorithm is designed and
analysed and run on a MIMD parallel computer namely the Balance 8000
Initially, an overview of the learning algorithms of ANNs are described.
Some of the acceleration techniques including Heuristic methods for the BP
like algorithms are introduced.
The software design of the simulator for both On-line and Batch BP
is described. Two different strategies for parallelism are considered and the
results of the speedups of both algorithms are compared.
Later a Heuristic algorithm (GRBH) for accelerating the BP method
is introduced and the results are compared with the BP using a variety of
The simulator is used to train networks for invariant character
recognition using moments. The trained networks are tested for different
examples and the results are analysed.
The thesis concludes with a chapter summarizing the main results and
suggestions for further study.
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.