By Topic

BCN: an architecture for weightless RAM-based neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Howells, G. ; Electron. Eng. Labs., Kent Univ., Canterbury, UK ; Fairhurst, M.C. ; Bisset, D.L.

This paper introduces a novel networking strategy for RAM-based neurons which significantly improves the training and recognition performance of such networks whilst maintaining the generalisation capabilities achieved in previous network configurations. The Boolean convergent network (BCN) is a RAM-based neural network where the inputs and output of the component neurons are taken from the values `0', `1' and the undefined value `u'. The inputs to a neuron form an addressable set incorporating all memory locations which may be formed by treating any undefined value within the input as either a `0' or a `1'. The output of a neuron can be any defined value which occurs exclusively within the memory locations included within the addressable set. If the addressable set contains either no defined value or examples of both defined values, then the undefined value `u' is output

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:3 )

Date of Conference:

27 Jun-2 Jul 1994