Associative neural memories
Digital Image Processing
Image Processing - Principles and Applications
Image Processing - Principles and Applications
A Bidirectional Hetero-Associative Memory for True-Color Patterns
Neural Processing Letters
A class of sparsely connected autoassociative morphological memories for large color images
IEEE Transactions on Neural Networks
Color image associative memory on a class of Cohen-Grossberg networks
Pattern Recognition
Quantale Modules and their Operators, with Applications
Journal of Logic and Computation
Implicative Fuzzy Associative Memories
IEEE Transactions on Fuzzy Systems
Morphological associative memories
IEEE Transactions on Neural Networks
Gray-scale morphological associative memories
IEEE Transactions on Neural Networks
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A quantale is the mathematical structure obtained by enriching a complete lattice with an associative binary operation which commutes with the supremum operation. We refer to an associative memory model that performs operations in a quantale as a quantale-based associative memory (QAM). Examples of QAMs include many lattice-based models such as gray-scale morphological associative memories and implicative fuzzy associative memories. Besides introducing auto-associative QAMs, this paper presents a QAM model for the storage and recall of color patterns. Specifically, novel QAM models, referred to as spherical CIELab QAMs, are defined in terms of the spherical coordinates of the CIELab system with an ordering scheme and a binary operation that yields a quantale. Computational experiments reveal that the spherical CIELab QAMs exhibit some tolerance with respect to impulsive noise.