Generalized Recurrent Exponential Fuzzy Associative Memories Based on Similarity Measures

DOI

10.2991/ifsa-eusflat-15.2015.66How to use a DOI?

Keywords

Associative memory, recurrent neural network, fuzzy system, similarity measure.

Abstract

The recurrent exponential fuzzy associative memory (RE-FAM) can be viewed as a recurrent neural network that employs a fuzzy similarity measure in its hidden layer. This paper introduces the generalized recurrent exponential fuzzy associative memory (GRE-FAM). In contrast to the RE-FAM, the GREFAM is equipped with a second hidden layer that is geared to avoiding crosstalk. Apart from theoretical results, this paper includes some computational experiments concerning the econstruction of corrupted gray-scale images.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Aline Cristina de Souza
AU  - Marcos Eduardo Valle
AU  - Peter Sussner
PY  - 2015/06
DA  - 2015/06
TI  - Generalized Recurrent Exponential Fuzzy Associative Memories Based on Similarity Measures
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 455
EP  - 462
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.66
DO  - 10.2991/ifsa-eusflat-15.2015.66
ID  - Souza2015/06
ER  -