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Generalized Encoding and Decoding Operators for Lattice-Based Associative Memories

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2 Author(s)
John McElroy ; Comput. & Inf. Sci. & Eng. Dept., Univ. of Florida, Gainesville, FL, USA ; Paul Gader

During the 1990s, Ritter introduced a new family of associative memories based on lattice algebra instead of linear algebra. These memories provide unlimited storage capacity, unlike linear-correlation-based models. The canonical lattice-based memories, however, are susceptible to noise in the initial input data. In this brief, we present novel methods of encoding and decoding lattice-based memories using two families of ordered weighted average (OWA) operators. The result is a greater robustness to distortion in the initial input data, and a greater understanding of the effect of the choice of encoding and decoding operators on the behavior of the system, with the tradeoff that the time complexity for encoding is increased.

Published in:

IEEE Transactions on Neural Networks  (Volume:20 ,  Issue: 10 )