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Paper details
Number 2 - June 2023
Volume 33 - 2023
Binary associative memories with complemented operations
Arturo Gamino-Carranza
Abstract
Associative memories based on lattice algebra are of great interest in pattern recognition applications due to their excellent
storage and recall properties. In this paper, a class of binary associative memory derived from lattice memories is presented,
which is based on the definition of new complemented binary operations and threshold unary operations. The new learning
method generates memories M and W; the former is robust to additive noise and the latter is robust to subtractive noise. In the recall step, the memories converge in a single step and use the same operation as the learning method. The storage capacity is unlimited, and in autoassociative mode there is perfect recall for the training set. Simulation results suggest that the proposed memories have better performance compared to other models.
Keywords
associative memory, neural network, morphological associative memory, perfect recall