Morphological Associative Memories

Abstract

Morphological associative memories (MAMs) belong to the class of morphological neural networks that – in contrast to classical models of artificial neural networks – employ operators of mathematical morphology in order to aggregate the synaptic weights and the incoming signal. The original and most widely known MAM model as well as some recent modifications of this model can be formulated using matrix operation of a lattice algebra called minimax algebra that is closely related to the umbra approach of grayscale mathematical morphology. Fuzzy MAM models execute basic operations of fuzzy mathematical morphology in the recall phase. In this article, we briefly review the aforementioned MAM models and discuss some applications in classification, hyperspectral image analysis, and prediction.