Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Improving Leung's bidirectional learning rule for associative memories

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

1 Author(s)
Lenze, B. ; Dept. of Comput. Sci., Univ. of Appl. Sci., Dortmund, Germany

Leung (1994) introduced a perceptron-like learning rule to enhance the recall performance of bidirectional associative memories (BAMs). He proved that his so-called bidirectional learning scheme always yields a solution within a finite number of learning iterations in case that a solution exists. Unfortunately, in the setting of Leung a solution only exists in case that the training set is strongly linear separable by hyperplanes through the origin. We extend Leung's approach by considering conditionally strong linear separable sets allowing separating hyperplanes not containing the origin. Moreover, we deal with BAMs, which are generalized by defining so-called dilation and translation parameters enlarging their capacity, while leaving their complexity almost unaffected. The whole approach leads to a generalized bidirectional learning rule which generates BAMs with dilation and translation that perform perfectly on the training set in a case that the latter satisfies the conditionally strong linear separability assumption. Therefore, in the sense of Leung, we conclude with an optimal learning strategy which contains Leung's initial idea as a special case

Published in:

Neural Networks, IEEE Transactions on  (Volume:12 ,  Issue: 5 )