This thesis aims to understand how to design high performance, flexible and cost
effective neural computing systems and apply them to a variety of real-time
Systems of this type already exist for the support of a range of ANN models.
However, many of these designs have concentrated on optimising the architecture of
the neural processor and have generally neglected other important aspects. If these
neural systems are to be of practical benefit to researchers and allow complex neural
problems to be solved efficiently, all aspects of their design must be addressed. [Continues.]
A doctoral thesis submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy at Loughborough University.
Science and Engineering Research Council. British Telecom.