Generalized encoding and decoding operators for lattice-based associative memories

Authors:
John McElroy;Paul Gader
Affiliations:
Computer and Information Science and Engineering Department, University of Florida, Gainesville, FL;Computer and Information Science and Engineering Department, University of Florida, Gainesville, FL
Venue:
IEEE Transactions on Neural Networks
Year:
2009

Citing 6
Cited 0

Bidirectional associative memories

IEEE Transactions on Systems, Man and Cybernetics
On ordered weighted averaging aggregation operators in multicriteria decisionmaking

IEEE Transactions on Systems, Man and Cybernetics
Families of OWA operators

Fuzzy Sets and Systems
Quantum associative memory

Information Sciences—Informatics and Computer Science: An International Journal
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Morphological associative memories

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

During the 1990s, Ritter et al. 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.