A two-level hamming network for high performance associative memory.

N Ikeda; P Watta; M Artiklar; M H Hassoun

doi:10.1016/s0893-6080(01)00089-2

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A two-level hamming network for high performance associative memory.

N Ikeda·P Watta·M Artiklar·M H Hassoun

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Abstract

This paper presents an analysis of a two-level decoupled Hamming network, which is a high performance discrete-time/discrete-state associative memory model. The two-level Hamming memory generalizes the Hamming memory by providing for local Hamming distance computations in the first level and a voting mechanism in the second level. In this paper, we study the effect of system dimension, window size, and noise on the capacity and error correction capability of the two-level Hamming memory. Simulation results are given for both random images and human face images.

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Algorithms Animals Artifacts Brain Face Humans Memory Models, Neurological Nerve Net Neural Networks, Computer Pattern Recognition, Visual Journal Article Research Support, U.S. Gov't, Non-P.H.S.

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N Ikeda, P Watta, M Artiklar, M H Hassoun. 2001. A two-level hamming network for high performance associative memory.. https://doi.org/10.1016/s0893-6080(01)00089-2

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