Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/243671
Type: Artigo
Title: Theta-fuzzy associative memories (theta-fams)
Author: Esmi, Estevão
Sussner, Peter
Bustince, Humberto
Fernández, Javier
Abstract: Most fuzzy associative memories (FAMs) in the literature correspond to neural networks with a single layer of weights that distributively contains the information on associations to be stored. The main applications of these types of associative memory can be found in fuzzy rule-based systems. In contrast, T-fuzzy associative memories (T-FAMs) represent parametrized fuzzy neural networks with a hidden layer and these FAM models extend (dual) S-FAMs and SM-FAMs based on fuzzy subsethood and similarity measures. In this paper, we provide theoretical results concerning the storage capacity and error correction capability of T-FAMs. In addition, we introduce a training algorithm for T-FAMs and we compare the error rates produced by T-FAMs and some well-known classifiers in some benchmark classification problems that are available on the internet. Finally, we apply T-FAMs to a problem of vision-based self-localization in mobile robotics.
Most fuzzy associative memories (FAMs) in the literature correspond to neural networks with a single layer of weights that distributively contains the information on associations to be stored. The main applications of these types of associative memory can be found in fuzzy rule-based systems. In contrast, Θ-fuzzy associative memories (Θ-FAMs) represent parametrized fuzzy neural networks with a hidden layer and these FAM models extend (dual) S-FAMs and SM-FAMs based on fuzzy subsethood and similarity measures. In this paper, we provide theoretical results concerning the storage capacity and error correction capability of Θ-FAMs. In addition, we introduce a training algorithm for Θ-FAMs and we compare the error rates produced by Θ-FAMs and some wellknown classifiers in some benchmark classification problems that are available on the internet. Finally, we apply Θ-FAMs to a problem of vision-based self-localization in mobile robotics.
Subject: Classificação
Memória associativa
Redes neurais (Computação)
Sistemas de reconhecimento de padrões
Visão de robô
Conjuntos fuzzy
Country: Estados Unidos
Editor: Institute of Electrical and Electronics Engineers
Citation: Theta-fuzzy Associative Memories (theta-fams). Ieee-inst Electrical Electronics Engineers Inc, v. 23, p. 313-326 APR-2015.
Rights: fechado
Identifier DOI: 10.1109/TFUZZ.2014.2312131
Address: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6767099
Date Issue: 2015
Appears in Collections:IMECC - Artigos e Outros Documentos

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