Theta-Fuzzy Associative Memories (Theta-FAMs)


Most fuzzy associative reminiscences (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 most applications of these varieties of associative memory can be found in fuzzy rule-based mostly systems. In distinction, Θ-fuzzy associative recollections ( 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. During this paper, we have a tendency to give theoretical results regarding the storage capability and error correction capability of Θ-FAMs. Moreover, we introduce a training algorithm for Θ-FAMs and we compare the error rates made by Θ-FAMs and some well-known classifiers in some benchmark classification problems that are obtainable on the internet. Finally, we have a tendency to apply Θ-FAMs to a downside of vision-based self-localization in mobile robotics.

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