Abstract
We have recently proven that many well-known fuzzy associative memory (FAM) models can be classified as (fuzzy) morphological neural networks (MNNs) because they perform an operation of (fuzzy) mathematical morphology at every node, possibly followed by the application of an activation function. One of the basic (fuzzy) morphological operators called (fuzzy) erosion is defined in terms of a (fuzzy) inclusion measure. In this paper, we take advantage of these considerations in order to derive a new non-distributive fuzzy morphological associative memory model on the basis of the Kosko subsethood measure that we named Kosko subsethood fuzzy associative memory (KS-FAM). After a brief discussion of the properties of the KS-FAM we compare the error correction capabilities of the KS-FAM and other fuzzy and gray-scale associative memories in terms of some experimental results concerning gray-scale image reconstruction.
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