Theta-Fuzzy Associative Memories (Theta-FAMs)

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.