On the Recall Capability of Recurrent Exponential Fuzzy Associative Memories Based on Similarity Mea

• Autores: M E Valle, A C de Souza
• Localización: Mathware & soft computing: The Magazine of the European Society for Fuzzy Logic and Technology, ISSN-e 1134-5632, Vol. 22, Nº. 1, 2015, págs. 33-39
• Idioma: inglés
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• Resumen

  • Recurrent exponential fuzzy associative memories (RE-FAMs) are non-distributive memory models derived from the multivalued exponential recurrent associative memory (MERAM) of Chiueh and Tsai. A RE-FAM defines recursively a sequence of fuzzy sets obtained by a weighted average of the fundamental memories. In this paper, we show that the output of a single-step RE-FAM can be made as close as desired to a certain convex combination of the fundamental memories most similar to the input. This paper also addresses the storage and recall capability of RE-FAMs. Precisely, computational experiments reveal that RE-FAMs can be effectively used for the retrieval of gray-scale images corrupted by either Gaussian noise or salt and pepper noise.