Generalized Recurrent Exponential Fuzzy Associative Memories Based on Similarity Measures

摘要

The recurrent exponential fuzzy associative memory (RE-FAM) can be viewed as a recurrent neural network that employs a fuzzy similarity measure in its hidden layer. This paper introduces the generalized recurrent exponential fuzzy associative memory (GRE-FAM). In contrast to the RE-FAM, the GREFAM is equipped with a second hidden layer that is geared to avoiding crosstalk. Apart from theoretical results, this paper includes some computational experiments concerning the reconstruction of corrupted gray-scale images.