Capacity analysis for a two-level decoupled Hamming network for associative memory under a noisy environment.

Capacity analysis for a two-level decoupled Hamming network for associative memory under a noisy environment. Research Abstract Details 

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Capacity analysis for a two-level decoupled Hamming network for associative memory under a noisy environment. Abstract Text:

Liang Chen,Naoyuki Tokuda,Akira Nagai,Liang Chen,Naoyuki Tokuda,Akira Nagai,

Our detailed analysis has established that in addition to the advantages of computationally efficiency and easy hardware implementation, the two-level decoupled Hamming network possesses a substantially higher capacity over the single-level Hamming associative memory since the effect caused by Ikeda et al.'s uniform random noise [Ikeda, N., Watta, P., Artiklar, M., & Hassoun, M. (2001). A two-level Hamming network for high performance associative memory. Neural Networks, 14(9), 1189-1200] is much smaller than that caused by the practically more prevalent concentrated noise. We therefore conclude that the two-level decoupled Hamming network with middle-sized windows should be an elegant associative memory model in all the senses of efficiency, hardware implementation and capacity.

Capacity analysis for a two-level decoupled Hamming network for associative memory under a noisy environment. Publishing Authors By Initials

L Chen,N Tokuda,A Nagai,L Chen,N Tokuda,A Nagai,

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Capacity analysis for a two-level decoupled Hamming network for associative memory under a noisy environment. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Neural networks : the official journal of the Inte

VOLUME: 20

Page Numbers: 598-609

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ISSN: 0893-6080

DAY: 11

MONTH: 02

YEAR: 2007

Capacity analysis for a two-level decoupled Hamming network for associative memory under a noisy environment. Information

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LANGUAGE: eng

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Grant and Affiliation Information for Capacity analysis for a two-level decoupled Hamming network for associative memory under a noisy environment.

AFFILIATION: Computer Science Department, University of Northern British Columbia, BC, Canada V2N 4Z9.

Country: United States

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MEDLINETA: Neural Netw

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