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 applications. 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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:387410 |
| Date | January 1994 |
| Creators | Naylor, David C. J. |
| Publisher | Loughborough University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://dspace.lboro.ac.uk/2134/31968 |
Page generated in 0.0022 seconds