Introduction to implicative fuzzy associative memories
Marcos Eduardo Valle, Peter Sussner, Fernando A. C. Gomide · 2005
Associative neural memories are models of biological phenomena that allow for the storage of pattern associations and the retrieval of the desired output pattern upon presentation of a possibly noisy or incomplete version of an input pattern. In this paper, we introduce implicative fuzzy associative memories (IFAM's), a class of associative neural memories models based on fuzzy set theory. An IFAM consists of a network of completely interconnected Pedrycz logic neurons whose connection weights are determined by the minimum of implications of presynaptic and postsynaptic activations. We present a series of results for autoassociative models including one pass convergence, unlimited storage capacity and tolerance with respect to eroded patterns.