  Max-C and min-D projection autoassociative fuzzy morphological memories
(max-C and min-D PAFMMs) are two layer feedforward fuzzy morphological neural
networks able to implement an associative memory designed for the storage and
retrieval of finite fuzzy sets or vectors on a hypercube. In this paper we
address the main features of these autoassociative memories, which include
unlimited absolute storage capacity, fast retrieval of stored items, few
spurious memories, and an excellent tolerance to either dilative noise or
erosive noise. Particular attention is given to the so-called PAFMM of Zadeh
which, besides performing no floating-point operations, exhibit the largest
noise tolerance among max-C and min-D PAFMMs. Computational experiments reveal
that Zadeh's max-C PFAMM, combined with a noise masking strategy, yields a fast
and robust classifier with strong potential for face recognition.
