An introduction to tunable equivalence fuzzy associative memories

In this paper, we present a new class of fuzzy associative memories (FAMs) called tunable equivalence fuzzy associative memories, for short tunable E-FAMs or TE-FAMs, that belong to the class Θ-fuzzy associative memories (Θ-FAMs). Recall that 0-FAMs represent fuzzy neural networks having a competitive hidden layer and weights that can be adjusted via a training algorithm. Like any associative memory model, Θ-FAMs depend on the specification of a fundamental memory set. In contrast to other Θ-FAM models, TE-FAMs make use of parametrized fuzzy equivalence measures that are associated with the hidden nodes and allow for the extraction of a fundamental memory set from the training data. The use of a smaller fundamental memory set than in previous articles on Θ-FAMs reduces the computational effort involved in deriving the weights without decreasing the quality of the results.