DocumentCode :
1369615
Title :
Generalized Encoding and Decoding Operators for Lattice-Based Associative Memories
Author :
McElroy, J. ; Gader, P.
Author_Institution :
Comput. & Inf. Sci. & Eng. Dept., Univ. of Florida, Gainesville, FL, USA
Volume :
20
Issue :
10
fYear :
2009
Firstpage :
1674
Lastpage :
1678
Abstract :
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.
Keywords :
computational complexity; content-addressable storage; decoding; encoding; linear algebra; mathematical operators; storage management; canonical lattice-based memories; generalized decoding operator; generalized encoding operator; lattice algebra; lattice-based associative memories; linear algebra; linear-correlation-based model; ordered weighted average operator; unlimited storage capacity; Artificial neural networks; Associative memory; Decoding; Encoding; Humans; Lattices; Linear algebra; Motion pictures; Noise robustness; Open wireless architecture; Associative; lattice; memory; ordered weighted average (OWA); Algorithms; Association Learning; Biomimetics; Computer Simulation; Feedback; Information Storage and Retrieval; Models, Theoretical; Neural Networks (Computer);
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2009.2028424
Filename :
5238638
Link To Document :
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