Pattern Classification using Generalized Recurrent Exponential Fuzzy Associative Memories
Marcos Eduardo Valle, Aline Cristina de Souza · Gate to Computer Science and Research · 2016
Generalized recurrent exponential fuzzy associative memories (GRE-FAMs) are biologically inspired models designed for the storage and recall of fuzzy sets.They can be viewed as a recurrent multilayer neural network that employs a fuzzy similarity measure in its first hidden layer.In this chapter, we provide theoretical results concerning the storage capacity and noise tolerance of a single-step GRE-FAM.Furthermore, we describe how a GRE-FAM model can be applied for pattern classification.Computational experiments show that the accuracy of certain GRE-FAM classifiers is competitive with some well-known classifiers from the literature.