By Topic

Designing associative memories implemented via recurrent neural networks for pattern recognition

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
$33 $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

5 Author(s)
J. A. Ruz-Hernandez ; Universidad Autonoma del Carmen, Facultad de Ingenieria, Cd. del Carmen, Campeche, Mexico ; M. U. Suarez-Duran ; R. Garcia-Hernandez ; E. Shelomov
more authors

In this paper a recurrent neural network is used as associative memory for pattern recognition. The goal of associative memory is to retrieve a stored pattern when enough information is presented in the network input. The network is training with twelve bipolar patterns to determine the corresponding weights. The weights are calculated by means of support vector machines training algorithms as the optimal hyperplane and soft margin hyperplane. Once the neural network is trained its performance is evaluated to retrieval stored patterns which correspond to characters encoded as bipolar vectors.

Published in:

Neural Networks (IJCNN), The 2011 International Joint Conference on

Date of Conference:

July 31 2011-Aug. 5 2011