Lorrentz, P. and Howells, W.G.J. and McDonald-Maier, K.D. (2008) An Analysis of Hardware Configurations for an Adaptive Weightless Neural Network. In: Ao, S.I. and Gelman, L. and Hukins, D.W.L. and Hunter, A. and Korsunsky, A.M., eds. World Congress on Engineering 2008. Lecture Notes in Engineering and Computer Science , I-II. IAE, INT ASSOC ENGINEERS-IAENG, UNIT1, 1-F, 37-39 HUNG TO ROAD, KWUN TONG, HONG KONG, 00000, PEOPLES R CHINA pp. 66-71. ISBN 978-988-98671-9-5 .
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This paper examines the potential offered by adaptive hardware configurations of a class of weightless neural architecture called the Enhanced Probabilistic Convergent Network targeted on a Virtex-II pro FPGA which is re configurable. The reconfiguration and adaptive capability of the Enhanced Probabilistic Convergent Network is a highly adaptive architecture offering a very fast, automated uninterrupted responses in potentially electronically harsh and isolated conditions. The hardware architecture is tested on a benchmark of unconstrained handwritten numerals from the Centre of Excellence for Document Analysis and Recognition.
|Item Type:||Conference or workshop item (Paper)|
|Additional information:||World Congress on Engineering 2008 Imperial Coll London, London, ENGLAND, JUL 02-04, 2008|
|Uncontrolled keywords:||adaptive neural network; FPGA; handwritten characters; reconfiguration|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Image and Information Engineering|
|Depositing User:||Jenny Harries|
|Date Deposited:||18 Apr 2009 11:39|
|Last Modified:||02 Oct 2009 07:42|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/14765 (The current URI for this page, for reference purposes)|
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