Title :
Theta-Fuzzy Associative Memories (Theta-FAMs)
Author :
Esmi, Estevao ; Sussner, Peter ; Bustince, Humberto ; Fernandez, Javier
Author_Institution :
Dept. of Appl. Math., Univ. of Campinas, Campinas, Brazil
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, 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 well-known 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.
Keywords :
content-addressable storage; fuzzy neural nets; fuzzy set theory; knowledge based systems; mobile robots; path planning; pattern classification; robot vision; Θ-fuzzy associative memories; SM-FAM; benchmark classification problems; classifiers; error correction capability; fuzzy rule-based systems; fuzzy subsethood; mobile robotics; parametrized fuzzy neural networks; similarity measures; storage capacity; theta-FAM; theta-fuzzy associative memories; training algorithm; vision-based self-localization; Associative memory; Atmospheric measurements; Equations; Indexes; Lattices; Mathematical model; Training; Classification; fuzzy associative memory (FAM); fuzzy similarity measure; fuzzy subsethood measure; robotics; vision-based self-localization;
Journal_Title :
Fuzzy Systems, IEEE Transactions on
DOI :
10.1109/TFUZZ.2014.2312131
